Apparatus and method for performing non-local means filtering using motion estimation circuitry of a graphics processor

ABSTRACT

Apparatus and method for non-local means filtering using a media processing block of a graphics processor. For example, one embodiment of a processor comprises: ray tracing circuitry to execute a first set of one or more commands to traverse rays through a bounding volume hierarchy (BVH) to identify BVH nodes and/or primitives intersected by the ray; shader execution circuitry to execute one or more shaders responsive to a second set of one or more commands to render a sequence of image frames based on the BVH nodes and/or primitives intersected by the ray; and a media processor comprising motion estimation circuitry to execute a third set of one or more commands to perform non-local means filtering to remove noise from the sequence of image frames based on a mean pixel value collected across the sequence of image frames.

BACKGROUND Field of the Invention

This invention relates generally to the field of graphics processors.More particularly, the invention relates to an apparatus and method forperforming a stable and short-latency sorting operation.

Description of the Related Art

Ray tracing is a technique in which a light transport is simulatedthrough physically-based rendering. Widely used in cinematic rendering,it was considered too resource-intensive for real-time performance untiljust a few years ago. One of the key operations in ray tracing isprocessing a visibility query for ray-scene intersections known as “raytraversal” which computes ray-scene intersections by traversing andintersecting nodes in a bounding volume hierarchy (BVH).

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained from thefollowing detailed description in conjunction with the followingdrawings, in which:

FIG. 1 is a block diagram of a computer system with a processor havingone or more processor cores and graphics processors;

FIGS. 2A-2D illustrate computing systems and graphics processorsprovided by embodiments described herein;

FIGS. 3A-3C illustrate block diagrams of additional graphics processorand compute accelerator architectures provided by embodiments describedherein;

FIG. 4 is a block diagram of a graphics-processing engine for a graphicsprocessor;

FIGS. 5A-5B illustrate thread execution logic 500 including an array ofprocessing elements employed in a graphics processor core according toembodiments described herein;

FIG. 6 illustrates an additional execution unit 600, according to anembodiment

FIG. 7 illustrates a graphics processor execution unit instructionformat;

FIG. 8 is a block diagram of a graphics processor which includes agraphics pipeline, a media pipeline, a display engine, thread executionlogic, and a render output pipeline;

FIG. 9A is a block diagram illustrating a graphics processor commandformat;

FIG. 9B is a block diagram illustrating a graphics processor commandsequence;

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system;

FIG. 11A illustrates example IP core development features of oneembodiment;

FIGS. 11B-D illustrate various packaging features for differentembodiments;

FIG. 12 illustrates an exemplary system on a chip integrated circuitthat may be fabricated using one or more IP cores;

FIG. 13 illustrates an exemplary graphics processor of a system on achip integrated circuit that may be fabricated using one or more IPcores;

FIG. 14 graphics processor 1340 includes the one or more MMU(s)1320A-1320B, cache(s) 1325A-1325B, and circuit interconnect(s)1330A-1330B of the graphics processor 1310 of FIG. 13 ;

FIG. 15 illustrates an architecture for performing initial training of amachine-learning architecture;

FIG. 16 illustrates how a machine-learning engine is continually trainedand updated during runtime;

FIG. 17 illustrates how a machine-learning engine is continually trainedand updated during runtime;

FIGS. 18A-B illustrate how machine learning data is shared on a network;and

FIG. 19 illustrates a method for training a machine-learning engine;

FIG. 20 illustrates how nodes exchange ghost region data to performdistributed denoising operations;

FIG. 21 illustrates an architecture in which image rendering anddenoising operations are distributed across a plurality of nodes;

FIG. 22 illustrates additional details of an architecture fordistributed rendering and denoising;

FIG. 23 illustrates a method for performing distributed rendering anddenoising;

FIG. 24 illustrates a machine learning method;

FIG. 25 illustrates a plurality of interconnected general purposegraphics processors;

FIG. 26 illustrates a set of convolutional layers and fully connectedlayers for a machine learning implementation;

FIG. 27 illustrates an example of a convolutional layer;

FIG. 28 illustrates an example of a set of interconnected nodes in amachine learning implementation;

FIG. 29 illustrates a training framework within which a neural networklearns using a training dataset;

FIG. 30A illustrates examples of model parallelism and data parallelism;

FIG. 30B illustrates a system on a chip (SoC);

FIG. 31 illustrates a processing architecture which includes ray tracingcores and tensor cores;

FIG. 32 illustrates an example of a beam;

FIG. 33 illustrates an apparatus for performing beam tracing;

FIG. 34 illustrates an example of a beam hierarchy;

FIG. 35 illustrates a method for performing beam tracing;

FIG. 36 illustrates an example of a distributed ray tracing engine;

FIGS. 37-38 illustrate compression performed in a ray tracing system;

FIG. 39 illustrates a method implemented on a ray tracing architecture;

FIG. 40 illustrates an exemplary hybrid ray tracing apparatus;

FIG. 41 illustrates stacks used for ray tracing operations;

FIG. 42 illustrates additional details for a hybrid ray tracingapparatus;

FIG. 43 illustrates a bounding volume hierarchy;

FIG. 44 illustrates a call stack and traversal state storage;

FIG. 45 illustrates a method for traversal and intersection;

FIGS. 46A-B illustrate how multiple dispatch cycles are required toexecute certain shaders;

FIG. 47 illustrates how a single dispatch cycle executes a plurality ofshaders;

FIG. 48 illustrates how a single dispatch cycle executes a plurality ofshaders;

FIG. 49 illustrates an architecture for executing ray tracinginstructions;

FIG. 50 illustrates a method for executing ray tracing instructionswithin a thread;

FIG. 51 illustrates one embodiment of an architecture for asynchronousray tracing;

FIG. 52 illustrates one embodiment of a ray traversal engine;

FIG. 53 illustrates one embodiment of the invention for performingnon-local means filtering; and

FIG. 54 illustrates a method in accordance with one embodiment of theinvention.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention described below. Itwill be apparent, however, to one skilled in the art that theembodiments of the invention may be practiced without some of thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form to avoid obscuring the underlyingprinciples of the embodiments of the invention.

Exemplary Graphics Processor Architectures and Data Types

System Overview

FIG. 1 is a block diagram of a processing system 100, according to anembodiment. System 100 may be used in a single processor desktop system,a multiprocessor workstation system, or a server system having a largenumber of processors 102 or processor cores 107. In one embodiment, thesystem 100 is a processing platform incorporated within asystem-on-a-chip (SoC) integrated circuit for use in mobile, handheld,or embedded devices such as within Internet-of-things (IoT) devices withwired or wireless connectivity to a local or wide area network.

In one embodiment, system 100 can include, couple with, or be integratedwithin: a server-based gaming platform; a game console, including a gameand media console; a mobile gaming console, a handheld game console, oran online game console. In some embodiments the system 100 is part of amobile phone, smart phone, tablet computing device or mobileInternet-connected device such as a laptop with low internal storagecapacity. Processing system 100 can also include, couple with, or beintegrated within: a wearable device, such as a smart watch wearabledevice; smart eyewear or clothing enhanced with augmented reality (AR)or virtual reality (VR) features to provide visual, audio or tactileoutputs to supplement real world visual, audio or tactile experiences orotherwise provide text, audio, graphics, video, holographic images orvideo, or tactile feedback; other augmented reality (AR) device; orother virtual reality (VR) device. In some embodiments, the processingsystem 100 includes or is part of a television or set top box device. Inone embodiment, system 100 can include, couple with, or be integratedwithin a self-driving vehicle such as a bus, tractor trailer, car, motoror electric power cycle, plane or glider (or any combination thereof).The self-driving vehicle may use system 100 to process the environmentsensed around the vehicle.

In some embodiments, the one or more processors 102 each include one ormore processor cores 107 to process instructions which, when executed,perform operations for system or user software. In some embodiments, atleast one of the one or more processor cores 107 is configured toprocess a specific instruction set 109. In some embodiments, instructionset 109 may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). One or more processor cores 107 may process adifferent instruction set 109, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 107may also include other processing devices, such as a Digital SignalProcessor (DSP).

In some embodiments, the processor 102 includes cache memory 104.Depending on the architecture, the processor 102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 102. In some embodiments, the processor 102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 107 using knowncache coherency techniques. A register file 106 can be additionallyincluded in processor 102 and may include different types of registersfor storing different types of data (e.g., integer registers, floatingpoint registers, status registers, and an instruction pointer register).Some registers may be general-purpose registers, while other registersmay be specific to the design of the processor 102.

In some embodiments, one or more processor(s) 102 are coupled with oneor more interface bus(es) 110 to transmit communication signals such asaddress, data, or control signals between processor 102 and othercomponents in the system 100. The interface bus 110, in one embodiment,can be a processor bus, such as a version of the Direct Media Interface(DMI) bus. However, processor busses are not limited to the DMI bus, andmay include one or more Peripheral Component Interconnect buses (e.g.,PCI, PCI express), memory busses, or other types of interface busses. Inone embodiment the processor(s) 102 include an integrated memorycontroller 116 and a platform controller hub 130. The memory controller116 facilitates communication between a memory device and othercomponents of the system 100, while the platform controller hub (PCH)130 provides connections to I/O devices via a local I/O bus.

The memory device 120 can be a dynamic random-access memory (DRAM)device, a static random-access memory (SRAM) device, flash memorydevice, phase-change memory device, or some other memory device havingsuitable performance to serve as process memory. In one embodiment thememory device 120 can operate as system memory for the system 100, tostore data 122 and instructions 121 for use when the one or moreprocessors 102 executes an application or process. Memory controller 116also couples with an optional external graphics processor 118, which maycommunicate with the one or more graphics processors 108 in processors102 to perform graphics and media operations. In some embodiments,graphics, media, and or compute operations may be assisted by anaccelerator 112 which is a coprocessor that can be configured to performa specialized set of graphics, media, or compute operations. Forexample, in one embodiment the accelerator 112 is a matrixmultiplication accelerator used to optimize machine learning or computeoperations. In one embodiment the accelerator 112 is a ray-tracingaccelerator that can be used to perform ray-tracing operations inconcert with the graphics processor 108. In one embodiment, an externalaccelerator 119 may be used in place of or in concert with theaccelerator 112.

In some embodiments a display device 111 can connect to the processor(s)102. The display device 111 can be one or more of an internal displaydevice, as in a mobile electronic device or a laptop device or anexternal display device attached via a display interface (e.g.,DisplayPort, etc.). In one embodiment the display device 111 can be ahead mounted display (HMD) such as a stereoscopic display device for usein virtual reality (VR) applications or augmented reality (AR)applications.

In some embodiments the platform controller hub 130 enables peripheralsto connect to memory device 120 and processor 102 via a high-speed I/Obus. The I/O peripherals include, but are not limited to, an audiocontroller 146, a network controller 134, a firmware interface 128, awireless transceiver 126, touch sensors 125, a data storage device 124(e.g., non-volatile memory, volatile memory, hard disk drive, flashmemory, NAND, 3D NAND, 3D XPoint, etc.). The data storage device 124 canconnect via a storage interface (e.g., SATA) or via a peripheral bus,such as a Peripheral Component Interconnect bus (e.g., PCI, PCIexpress). The touch sensors 125 can include touch screen sensors,pressure sensors, or fingerprint sensors. The wireless transceiver 126can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile networktransceiver such as a 3G, 4G, 5G, or Long-Term Evolution (LTE)transceiver. The firmware interface 128 enables communication withsystem firmware, and can be, for example, a unified extensible firmwareinterface (UEFI). The network controller 134 can enable a networkconnection to a wired network. In some embodiments, a high-performancenetwork controller (not shown) couples with the interface bus 110. Theaudio controller 146, in one embodiment, is a multi-channel highdefinition audio controller. In one embodiment the system 100 includesan optional legacy I/O controller 140 for coupling legacy (e.g.,Personal System 2 (PS/2)) devices to the system. The platform controllerhub 130 can also connect to one or more Universal Serial Bus (USB)controllers 142 connect input devices, such as keyboard and mouse 143combinations, a camera 144, or other USB input devices.

It will be appreciated that the system 100 shown is exemplary and notlimiting, as other types of data processing systems that are differentlyconfigured may also be used. For example, an instance of the memorycontroller 116 and platform controller hub 130 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 118. In one embodiment the platform controller hub 130 and/ormemory controller 116 may be external to the one or more processor(s)102. For example, the system 100 can include an external memorycontroller 116 and platform controller hub 130, which may be configuredas a memory controller hub and peripheral controller hub within a systemchipset that is in communication with the processor(s) 102.

For example, circuit boards (“sleds”) can be used on which componentssuch as CPUs, memory, and other components are placed are designed forincreased thermal performance. In some examples, processing componentssuch as the processors are located on a top side of a sled while nearmemory, such as DIMMs, are located on a bottom side of the sled. As aresult of the enhanced airflow provided by this design, the componentsmay operate at higher frequencies and power levels than in typicalsystems, thereby increasing performance. Furthermore, the sleds areconfigured to blindly mate with power and data communication cables in arack, thereby enhancing their ability to be quickly removed, upgraded,reinstalled, and/or replaced. Similarly, individual components locatedon the sleds, such as processors, accelerators, memory, and data storagedrives, are configured to be easily upgraded due to their increasedspacing from each other. In the illustrative embodiment, the componentsadditionally include hardware attestation features to prove theirauthenticity.

A data center can utilize a single network architecture (“fabric”) thatsupports multiple other network architectures including Ethernet andOmni-Path. The sleds can be coupled to switches via optical fibers,which provide higher bandwidth and lower latency than typical twistedpair cabling (e.g., Category 5, Category 5e, Category 6, etc.). Due tothe high bandwidth, low latency interconnections and networkarchitecture, the data center may, in use, pool resources, such asmemory, accelerators (e.g., GPUs, graphics accelerators, FPGAs, ASICs,neural network and/or artificial intelligence accelerators, etc.), anddata storage drives that are physically disaggregated, and provide themto compute resources (e.g., processors) on an as needed basis, enablingthe compute resources to access the pooled resources as if they werelocal.

A power supply or source can provide voltage and/or current to system100 or any component or system described herein. In one example, thepower supply includes an AC to DC (alternating current to directcurrent) adapter to plug into a wall outlet. Such AC power can berenewable energy (e.g., solar power) power source. In one example, powersource includes a DC power source, such as an external AC to DCconverter. In one example, power source or power supply includeswireless charging hardware to charge via proximity to a charging field.In one example, power source can include an internal battery,alternating current supply, motion-based power supply, solar powersupply, or fuel cell source.

FIGS. 2A-2D illustrate computing systems and graphics processorsprovided by embodiments described herein. The elements of FIGS. 2A-2Dhaving the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

FIG. 2A is a block diagram of an embodiment of a processor 200 havingone or more processor cores 202A-202N, an integrated memory controller214, and an integrated graphics processor 208. Processor 200 can includeadditional cores up to and including additional core 202N represented bythe dashed lined boxes. Each of processor cores 202A-202N includes oneor more internal cache units 204A-204N. In some embodiments eachprocessor core also has access to one or more shared cached units 206.The internal cache units 204A-204N and shared cache units 206 representa cache memory hierarchy within the processor 200. The cache memoryhierarchy may include at least one level of instruction and data cachewithin each processor core and one or more levels of shared mid-levelcache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or otherlevels of cache, where the highest level of cache before external memoryis classified as the LLC. In some embodiments, cache coherency logicmaintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or morebus controller units 216 and a system agent core 210. The one or morebus controller units 216 manage a set of peripheral buses, such as oneor more PCI or PCI express busses. System agent core 210 providesmanagement functionality for the various processor components. In someembodiments, system agent core 210 includes one or more integratedmemory controllers 214 to manage access to various external memorydevices (not shown).

In some embodiments, one or more of the processor cores 202A-202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 210 includes components for coordinating andoperating cores 202A-202N during multi-threaded processing. System agentcore 210 may additionally include a power control unit (PCU), whichincludes logic and components to regulate the power state of processorcores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphicsprocessor 208 to execute graphics processing operations. In someembodiments, the graphics processor 208 couples with the set of sharedcache units 206, and the system agent core 210, including the one ormore integrated memory controllers 214. In some embodiments, the systemagent core 210 also includes a display controller 211 to drive graphicsprocessor output to one or more coupled displays. In some embodiments,display controller 211 may also be a separate module coupled with thegraphics processor via at least one interconnect, or may be integratedwithin the graphics processor 208.

In some embodiments, a ring-based interconnect unit 212 is used tocouple the internal components of the processor 200. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 208 couples with the ring interconnect 212 via an I/O link213.

The exemplary I/O link 213 represents at least one of multiple varietiesof I/O interconnects, including an on package I/O interconnect whichfacilitates communication between various processor components and ahigh-performance embedded memory module 218, such as an eDRAM module. Insome embodiments, each of the processor cores 202A-202N and graphicsprocessor 208 can use embedded memory modules 218 as a shared Last LevelCache.

In some embodiments, processor cores 202A-202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 202A-202N are heterogeneous in terms of instruction setarchitecture (ISA), where one or more of processor cores 202A-202Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment, processor cores 202A-202N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. In one embodiment,processor cores 202A-202N are heterogeneous in terms of computationalcapability. Additionally, processor 200 can be implemented on one ormore chips or as an SoC integrated circuit having the illustratedcomponents, in addition to other components.

FIG. 2B is a block diagram of hardware logic of a graphics processorcore 219, according to some embodiments described herein. Elements ofFIG. 2B having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Thegraphics processor core 219, sometimes referred to as a core slice, canbe one or multiple graphics cores within a modular graphics processor.The graphics processor core 219 is exemplary of one graphics core slice,and a graphics processor as described herein may include multiplegraphics core slices based on target power and performance envelopes.Each graphics processor core 219 can include a fixed function block 230coupled with multiple sub-cores 221A-221F, also referred to assub-slices, that include modular blocks of general-purpose and fixedfunction logic.

In some embodiments, the fixed function block 230 includes ageometry/fixed function pipeline 231 that can be shared by all sub-coresin the graphics processor core 219, for example, in lower performanceand/or lower power graphics processor implementations. In variousembodiments, the geometry/fixed function pipeline 231 includes a 3Dfixed function pipeline (e.g., 3D pipeline 312 as in FIG. 3 and FIG. 4 ,described below) a video front-end unit, a thread spawner and threaddispatcher, and a unified return buffer manager, which manages unifiedreturn buffers (e.g., unified return buffer 418 in FIG. 4 , as describedbelow).

In one embodiment the fixed function block 230 also includes a graphicsSoC interface 232, a graphics microcontroller 233, and a media pipeline234. The graphics SoC interface 232 provides an interface between thegraphics processor core 219 and other processor cores within a system ona chip integrated circuit. The graphics microcontroller 233 is aprogrammable sub-processor that is configurable to manage variousfunctions of the graphics processor core 219, including thread dispatch,scheduling, and pre-emption. The media pipeline 234 (e.g., mediapipeline 316 of FIG. 3 and FIG. 4 ) includes logic to facilitate thedecoding, encoding, pre-processing, and/or post-processing of multimediadata, including image and video data. The media pipeline 234 implementmedia operations via requests to compute or sampling logic within thesub-cores 221-221F.

In one embodiment the SoC interface 232 enables the graphics processorcore 219 to communicate with general-purpose application processor cores(e.g., CPUs) and/or other components within an SoC, including memoryhierarchy elements such as a shared last level cache memory, the systemRAM, and/or embedded on-chip or on-package DRAM. The SoC interface 232can also enable communication with fixed function devices within theSoC, such as camera imaging pipelines, and enables the use of and/orimplements global memory atomics that may be shared between the graphicsprocessor core 219 and CPUs within the SoC. The SoC interface 232 canalso implement power management controls for the graphics processor core219 and enable an interface between a clock domain of the graphic core219 and other clock domains within the SoC. In one embodiment the SoCinterface 232 enables receipt of command buffers from a command streamerand global thread dispatcher that are configured to provide commands andinstructions to each of one or more graphics cores within a graphicsprocessor. The commands and instructions can be dispatched to the mediapipeline 234, when media operations are to be performed, or a geometryand fixed function pipeline (e.g., geometry and fixed function pipeline231, geometry and fixed function pipeline 237) when graphics processingoperations are to be performed.

The graphics microcontroller 233 can be configured to perform variousscheduling and management tasks for the graphics processor core 219. Inone embodiment the graphics microcontroller 233 can perform graphicsand/or compute workload scheduling on the various graphics parallelengines within execution unit (EU) arrays 222A-222F, 224A-224F withinthe sub-cores 221A-221F. In this scheduling model, host softwareexecuting on a CPU core of an SoC including the graphics processor core219 can submit workloads one of multiple graphic processor doorbells,which invokes a scheduling operation on the appropriate graphics engine.Scheduling operations include determining which workload to run next,submitting a workload to a command streamer, pre-empting existingworkloads running on an engine, monitoring progress of a workload, andnotifying host software when a workload is complete. In one embodimentthe graphics microcontroller 233 can also facilitate low-power or idlestates for the graphics processor core 219, providing the graphicsprocessor core 219 with the ability to save and restore registers withinthe graphics processor core 219 across low-power state transitionsindependently from the operating system and/or graphics driver softwareon the system.

The graphics processor core 219 may have greater than or fewer than theillustrated sub-cores 221A-221F, up to N modular sub-cores. For each setof N sub-cores, the graphics processor core 219 can also include sharedfunction logic 235, shared and/or cache memory 236, a geometry/fixedfunction pipeline 237, as well as additional fixed function logic 238 toaccelerate various graphics and compute processing operations. Theshared function logic 235 can include logic units associated with theshared function logic 420 of FIG. 4 (e.g., sampler, math, and/orinter-thread communication logic) that can be shared by each N sub-coreswithin the graphics processor core 219. The shared and/or cache memory236 can be a last-level cache for the set of N sub-cores 221A-221Fwithin the graphics processor core 219, and can also serve as sharedmemory that is accessible by multiple sub-cores. The geometry/fixedfunction pipeline 237 can be included instead of the geometry/fixedfunction pipeline 231 within the fixed function block 230 and caninclude the same or similar logic units.

In one embodiment the graphics processor core 219 includes additionalfixed function logic 238 that can include various fixed functionacceleration logic for use by the graphics processor core 219. In oneembodiment the additional fixed function logic 238 includes anadditional geometry pipeline for use in position only shading. Inposition-only shading, two geometry pipelines exist, the full geometrypipeline within the geometry/fixed function pipeline 238, 231, and acull pipeline, which is an additional geometry pipeline which may beincluded within the additional fixed function logic 238. In oneembodiment the cull pipeline is a trimmed down version of the fullgeometry pipeline. The full pipeline and the cull pipeline can executedifferent instances of the same application, each instance having aseparate context. Position only shading can hide long cull runs ofdiscarded triangles, enabling shading to be completed earlier in someinstances. For example and in one embodiment the cull pipeline logicwithin the additional fixed function logic 238 can execute positionshaders in parallel with the main application and generally generatescritical results faster than the full pipeline, as the cull pipelinefetches and shades only the position attribute of the vertices, withoutperforming rasterization and rendering of the pixels to the framebuffer. The cull pipeline can use the generated critical results tocompute visibility information for all the triangles without regard towhether those triangles are culled. The full pipeline (which in thisinstance may be referred to as a replay pipeline) can consume thevisibility information to skip the culled triangles to shade only thevisible triangles that are finally passed to the rasterization phase.

In one embodiment the additional fixed function logic 238 can alsoinclude machine-learning acceleration logic, such as fixed functionmatrix multiplication logic, for implementations including optimizationsfor machine learning training or inferencing.

Within each graphics sub-core 221A-221F includes a set of executionresources that may be used to perform graphics, media, and computeoperations in response to requests by graphics pipeline, media pipeline,or shader programs. The graphics sub-cores 221A-221F include multiple EUarrays 222A-222F, 224A-224F, thread dispatch and inter-threadcommunication (TD/IC) logic 223A-223F, a 3D (e.g., texture) sampler225A-225F, a media sampler 206A-206F, a shader processor 227A-227F, andshared local memory (SLM) 228A-228F. The EU arrays 222A-222F, 224A-224Feach include multiple execution units, which are general-purposegraphics processing units capable of performing floating-point andinteger/fixed-point logic operations in service of a graphics, media, orcompute operation, including graphics, media, or compute shaderprograms. The TD/IC logic 223A-223F performs local thread dispatch andthread control operations for the execution units within a sub-core andfacilitate communication between threads executing on the executionunits of the sub-core. The 3D sampler 225A-225F can read texture orother 3D graphics related data into memory. The 3D sampler can readtexture data differently based on a configured sample state and thetexture format associated with a given texture. The media sampler206A-206F can perform similar read operations based on the type andformat associated with media data. In one embodiment, each graphicssub-core 221A-221F can alternately include a unified 3D and mediasampler. Threads executing on the execution units within each of thesub-cores 221A-221F can make use of shared local memory 228A-228F withineach sub-core, to enable threads executing within a thread group toexecute using a common pool of on-chip memory.

FIG. 2C illustrates a graphics processing unit (GPU) 239 that includesdedicated sets of graphics processing resources arranged into multi-coregroups 240A-240N. While the details of only a single multi-core group240A are provided, it will be appreciated that the other multi-coregroups 240B-240N may be equipped with the same or similar sets ofgraphics processing resources.

As illustrated, a multi-core group 240A may include a set of graphicscores 243, a set of tensor cores 244, and a set of ray tracing cores245. A scheduler/dispatcher 241 schedules and dispatches the graphicsthreads for execution on the various cores 243, 244, 245. A set ofregister files 242 store operand values used by the cores 243, 244, 245when executing the graphics threads. These may include, for example,integer registers for storing integer values, floating point registersfor storing floating point values, vector registers for storing packeddata elements (integer and/or floating point data elements) and tileregisters for storing tensor/matrix values. In one embodiment, the tileregisters are implemented as combined sets of vector registers.

One or more combined level 1 (L1) caches and shared memory units 247store graphics data such as texture data, vertex data, pixel data, raydata, bounding volume data, etc., locally within each multi-core group240A. One or more texture units 247 can also be used to performtexturing operations, such as texture mapping and sampling. A Level 2(L2) cache 253 shared by all or a subset of the multi-core groups240A-240N stores graphics data and/or instructions for multipleconcurrent graphics threads. As illustrated, the L2 cache 253 may beshared across a plurality of multi-core groups 240A-240N. One or morememory controllers 248 couple the GPU 239 to a memory 249 which may be asystem memory (e.g., DRAM) and/or a dedicated graphics memory (e.g.,GDDR6 memory).

Input/output (I/O) circuitry 250 couples the GPU 239 to one or more I/Odevices 252 such as digital signal processors (DSPs), networkcontrollers, or user input devices. An on-chip interconnect may be usedto couple the I/O devices 252 to the GPU 239 and memory 249. One or moreI/O memory management units (IOMMUs) 251 of the I/O circuitry 250 couplethe I/O devices 252 directly to the system memory 249. In oneembodiment, the IOMMU 251 manages multiple sets of page tables to mapvirtual addresses to physical addresses in system memory 249. In thisembodiment, the I/O devices 252, CPU(s) 246, and GPU(s) 239 may sharethe same virtual address space.

In one implementation, the IOMMU 251 supports virtualization. In thiscase, it may manage a first set of page tables to map guest/graphicsvirtual addresses to guest/graphics physical addresses and a second setof page tables to map the guest/graphics physical addresses tosystem/host physical addresses (e.g., within system memory 249). Thebase addresses of each of the first and second sets of page tables maybe stored in control registers and swapped out on a context switch(e.g., so that the new context is provided with access to the relevantset of page tables). While not illustrated in FIG. 2C, each of the cores243, 244, 245 and/or multi-core groups 240A-240N may include translationlookaside buffers (TLBs) to cache guest virtual to guest physicaltranslations, guest physical to host physical translations, and guestvirtual to host physical translations.

In one embodiment, the CPUs 246, GPUs 239, and I/O devices 252 areintegrated on a single semiconductor chip and/or chip package. Theillustrated memory 249 may be integrated on the same chip or may becoupled to the memory controllers 248 via an off-chip interface. In oneimplementation, the memory 249 comprises GDDR6 memory which shares thesame virtual address space as other physical system-level memories,although the underlying principles of the invention are not limited tothis specific implementation.

In one embodiment, the tensor cores 244 include a plurality of executionunits specifically designed to perform matrix operations, which are thefundamental compute operation used to perform deep learning operations.For example, simultaneous matrix multiplication operations may be usedfor neural network training and inferencing. The tensor cores 244 mayperform matrix processing using a variety of operand precisionsincluding single precision floating-point (e.g., 32 bits),half-precision floating point (e.g., 16 bits), integer words (16 bits),bytes (8 bits), and half-bytes (4 bits). In one embodiment, a neuralnetwork implementation extracts features of each rendered scene,potentially combining details from multiple frames, to construct ahigh-quality final image.

In deep learning implementations, parallel matrix multiplication workmay be scheduled for execution on the tensor cores 244. The training ofneural networks, in particular, requires a significant number matrix dotproduct operations. In order to process an inner-product formulation ofan N×N×N matrix multiply, the tensor cores 244 may include at least Ndot-product processing elements. Before the matrix multiply begins, oneentire matrix is loaded into tile registers and at least one column of asecond matrix is loaded each cycle for N cycles. Each cycle, there are Ndot products that are processed.

Matrix elements may be stored at different precisions depending on theparticular implementation, including 16-bit words, 8-bit bytes (e.g.,INT8) and 4-bit half-bytes (e.g., INT4). Different precision modes maybe specified for the tensor cores 244 to ensure that the most efficientprecision is used for different workloads (e.g., such as inferencingworkloads which can tolerate quantization to bytes and half-bytes).

In one embodiment, the ray tracing cores 245 accelerate ray tracingoperations for both real-time ray tracing and non-real-time ray tracingimplementations. In particular, the ray tracing cores 245 include raytraversal/intersection circuitry for performing ray traversal usingbounding volume hierarchies (BVHs) and identifying intersections betweenrays and primitives enclosed within the BVH volumes. The ray tracingcores 245 may also include circuitry for performing depth testing andculling (e.g., using a Z buffer or similar arrangement). In oneimplementation, the ray tracing cores 245 perform traversal andintersection operations in concert with the image denoising techniquesdescribed herein, at least a portion of which may be executed on thetensor cores 244. For example, in one embodiment, the tensor cores 244implement a deep learning neural network to perform denoising of framesgenerated by the ray tracing cores 245. However, the CPU(s) 246,graphics cores 243, and/or ray tracing cores 245 may also implement allor a portion of the denoising and/or deep learning algorithms.

In addition, as described above, a distributed approach to denoising maybe employed in which the GPU 239 is in a computing device coupled toother computing devices over a network or high speed interconnect. Inthis embodiment, the interconnected computing devices share neuralnetwork learning/training data to improve the speed with which theoverall system learns to perform denoising for different types of imageframes and/or different graphics applications.

In one embodiment, the ray tracing cores 245 process all BVH traversaland ray-primitive intersections, saving the graphics cores 243 frombeing overloaded with thousands of instructions per ray. In oneembodiment, each ray tracing core 245 includes a first set ofspecialized circuitry for performing bounding box tests (e.g., fortraversal operations) and a second set of specialized circuitry forperforming the ray-triangle intersection tests (e.g., intersecting rayswhich have been traversed). Thus, in one embodiment, the multi-coregroup 240A can simply launch a ray probe, and the ray tracing cores 245independently perform ray traversal and intersection and return hit data(e.g., a hit, no hit, multiple hits, etc.) to the thread context. Theother cores 243, 244 are freed to perform other graphics or compute workwhile the ray tracing cores 245 perform the traversal and intersectionoperations.

In one embodiment, each ray tracing core 245 includes a traversal unitto perform BVH testing operations and an intersection unit whichperforms ray-primitive intersection tests. The intersection unitgenerates a “hit”, “no hit”, or “multiple hit” response, which itprovides to the appropriate thread. During the traversal andintersection operations, the execution resources of the other cores(e.g., graphics cores 243 and tensor cores 244) are freed to performother forms of graphics work.

In one particular embodiment described below, a hybrid rasterization/raytracing approach is used in which work is distributed between thegraphics cores 243 and ray tracing cores 245.

In one embodiment, the ray tracing cores 245 (and/or other cores 243,244) include hardware support for a ray tracing instruction set such asMicrosoft's DirectX Ray Tracing (DXR) which includes a DispatchRayscommand, as well as ray-generation, closest-hit, any-hit, and missshaders, which enable the assignment of unique sets of shaders andtextures for each object. Another ray tracing platform which may besupported by the ray tracing cores 245, graphics cores 243 and tensorcores 244 is Vulkan 1.1.85. Note, however, that the underlyingprinciples of the invention are not limited to any particular raytracing ISA.

In general, the various cores 245, 244, 243 may support a ray tracinginstruction set that includes instructions/functions for ray generation,closest hit, any hit, ray-primitive intersection, per-primitive andhierarchical bounding box construction, miss, visit, and exceptions.More specifically, one embodiment includes ray tracing instructions toperform the following functions:

Ray Generation—Ray generation instructions may be executed for eachpixel, sample, or other user-defined work assignment.

Closest Hit—A closest hit instruction may be executed to locate theclosest intersection point of a ray with primitives within a scene.

Any Hit—An any hit instruction identifies multiple intersections betweena ray and primitives within a scene, potentially to identify a newclosest intersection point.

Intersection—An intersection instruction performs a ray-primitiveintersection test and outputs a result.

Per-primitive Bounding box Construction—This instruction builds abounding box around a given primitive or group of primitives (e.g., whenbuilding a new BVH or other acceleration data structure).

Miss—Indicates that a ray misses all geometry within a scene, orspecified region of a scene.

Visit—Indicates the children volumes a ray will traverse.

Exceptions—Includes various types of exception handlers (e.g., invokedfor various error conditions).

FIG. 2D is a block diagram of general purpose graphics processing unit(GPGPU) 270 that can be configured as a graphics processor and/orcompute accelerator, according to embodiments described herein. TheGPGPU 270 can interconnect with host processors (e.g., one or moreCPU(s) 246) and memory 271, 272 via one or more system and/or memorybusses. In one embodiment the memory 271 is system memory that may beshared with the one or more CPU(s) 246, while memory 272 is devicememory that is dedicated to the GPGPU 270. In one embodiment, componentswithin the GPGPU 270 and device memory 272 may be mapped into memoryaddresses that are accessible to the one or more CPU(s) 246. Access tomemory 271 and 272 may be facilitated via a memory controller 268. Inone embodiment the memory controller 268 includes an internal directmemory access (DMA) controller 269 or can include logic to performoperations that would otherwise be performed by a DMA controller.

The GPGPU 270 includes multiple cache memories, including an L2 cache253, L1 cache 254, an instruction cache 255, and shared memory 256, atleast a portion of which may also be partitioned as a cache memory. TheGPGPU 270 also includes multiple compute units 260A-260N. Each computeunit 260A-260N includes a set of vector registers 261, scalar registers262, vector logic units 263, and scalar logic units 264. The computeunits 260A-260N can also include local shared memory 265 and a programcounter 266. The compute units 260A-260N can couple with a constantcache 267, which can be used to store constant data, which is data thatwill not change during the run of kernel or shader program that executeson the GPGPU 270. In one embodiment the constant cache 267 is a scalardata cache and cached data can be fetched directly into the scalarregisters 262.

During operation, the one or more CPU(s) 246 can write commands intoregisters or memory in the GPGPU 270 that has been mapped into anaccessible address space. The command processors 257 can read thecommands from registers or memory and determine how those commands willbe processed within the GPGPU 270. A thread dispatcher 258 can then beused to dispatch threads to the compute units 260A-260N to perform thosecommands. Each compute unit 260A-260N can execute threads independentlyof the other compute units. Additionally each compute unit 260A-260N canbe independently configured for conditional computation and canconditionally output the results of computation to memory. The commandprocessors 257 can interrupt the one or more CPU(s) 246 when thesubmitted commands are complete.

FIGS. 3A-3C illustrate block diagrams of additional graphics processorand compute accelerator architectures provided by embodiments describedherein. The elements of FIGS. 3A-3C having the same reference numbers(or names) as the elements of any other figure herein can operate orfunction in any manner similar to that described elsewhere herein, butare not limited to such.

FIG. 3A is a block diagram of a graphics processor 300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores, or other semiconductordevices such as, but not limited to, memory devices or networkinterfaces. In some embodiments, the graphics processor communicates viaa memory mapped I/O interface to registers on the graphics processor andwith commands placed into the processor memory. In some embodiments,graphics processor 300 includes a memory interface 314 to access memory.Memory interface 314 can be an interface to local memory, one or moreinternal caches, one or more shared external caches, and/or to systemmemory.

In some embodiments, graphics processor 300 also includes a displaycontroller 302 to drive display output data to a display device 318.Display controller 302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. The display device 318 can be an internal orexternal display device. In one embodiment the display device 318 is ahead mounted display device, such as a virtual reality (VR) displaydevice or an augmented reality (AR) display device. In some embodiments,graphics processor 300 includes a video codec engine 306 to encode,decode, or transcode media to, from, or between one or more mediaencoding formats, including, but not limited to Moving Picture ExpertsGroup (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formatssuch as H.264/MPEG-4 AVC, H.265/HEVC, Alliance for Open Media (AOMedia)VP8, VP9, as well as the Society of Motion Picture & TelevisionEngineers (SMPTE) 421M/VC-1, and Joint Photographic Experts Group (JPEG)formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block imagetransfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 310. In someembodiments, GPE 310 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 315.While 3D pipeline 312 can be used to perform media operations, anembodiment of GPE 310 also includes a media pipeline 316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 306. In some embodiments, media pipeline 316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executingthreads spawned by 3D pipeline 312 and media pipeline 316. In oneembodiment, the pipelines send thread execution requests to 3D/Mediasubsystem 315, which includes thread dispatch logic for arbitrating anddispatching the various requests to available thread executionresources. The execution resources include an array of graphicsexecution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 315 includes one or more internal cachesfor thread instructions and data. In some embodiments, the subsystemalso includes shared memory, including registers and addressable memory,to share data between threads and to store output data.

FIG. 3B illustrates a graphics processor 320 having a tiledarchitecture, according to embodiments described herein. In oneembodiment the graphics processor 320 includes a graphics processingengine cluster 322 having multiple instances of the graphics processingengine 310 of FIG. 3A within a graphics engine tile 310A-310D. Eachgraphics engine tile 310A-310D can be interconnected via a set of tileinterconnects 323A-323F. Each graphics engine tile 310A-310D can also beconnected to a memory module or memory device 326A-326D via memoryinterconnects 325A-325D. The memory devices 326A-326D can use anygraphics memory technology. For example, the memory devices 326A-326Dmay be graphics double data rate (GDDR) memory. The memory devices326A-326D, in one embodiment, are high-bandwidth memory (HBM) modulesthat can be on-die with their respective graphics engine tile 310A-310D.In one embodiment the memory devices 326A-326D are stacked memorydevices that can be stacked on top of their respective graphics enginetile 310A-310D. In one embodiment, each graphics engine tile 310A-310Dand associated memory 326A-326D reside on separate chiplets, which arebonded to a base die or base substrate, as described on further detailin FIGS. 11B-11D.

The graphics processing engine cluster 322 can connect with an on-chipor on-package fabric interconnect 324. The fabric interconnect 324 canenable communication between graphics engine tiles 310A-310D andcomponents such as the video codec 306 and one or more copy engines 304.The copy engines 304 can be used to move data out of, into, and betweenthe memory devices 326A-326D and memory that is external to the graphicsprocessor 320 (e.g., system memory). The fabric interconnect 324 canalso be used to interconnect the graphics engine tiles 310A-310D. Thegraphics processor 320 may optionally include a display controller 302to enable a connection with an external display device 318. The graphicsprocessor may also be configured as a graphics or compute accelerator.In the accelerator configuration, the display controller 302 and displaydevice 318 may be omitted.

The graphics processor 320 can connect to a host system via a hostinterface 328. The host interface 328 can enable communication betweenthe graphics processor 320, system memory, and/or other systemcomponents. The host interface 328 can be, for example a PCI express busor another type of host system interface.

FIG. 3C illustrates a compute accelerator 330, according to embodimentsdescribed herein. The compute accelerator 330 can include architecturalsimilarities with the graphics processor 320 of FIG. 3B and is optimizedfor compute acceleration. A compute engine cluster 332 can include a setof compute engine tiles 340A-340D that include execution logic that isoptimized for parallel or vector-based general-purpose computeoperations. In some embodiments, the compute engine tiles 340A-340D donot include fixed function graphics processing logic, although in oneembodiment one or more of the compute engine tiles 340A-340D can includelogic to perform media acceleration. The compute engine tiles 340A-340Dcan connect to memory 326A-326D via memory interconnects 325A-325D. Thememory 326A-326D and memory interconnects 325A-325D may be similartechnology as in graphics processor 320, or can be different. Thegraphics compute engine tiles 340A-340D can also be interconnected via aset of tile interconnects 323A-323F and may be connected with and/orinterconnected by a fabric interconnect 324. In one embodiment thecompute accelerator 330 includes a large L3 cache 336 that can beconfigured as a device-wide cache. The compute accelerator 330 can alsoconnect to a host processor and memory via a host interface 328 in asimilar manner as the graphics processor 320 of FIG. 3B.

Graphics Processing Engine

FIG. 4 is a block diagram of a graphics processing engine 410 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 410 is a version of theGPE 310 shown in FIG. 3A, and may also represent a graphics engine tile310A-310D of FIG. 3B. Elements of FIG. 4 having the same referencenumbers (or names) as the elements of any other figure herein canoperate or function in any manner similar to that described elsewhereherein, but are not limited to such. For example, the 3D pipeline 312and media pipeline 316 of FIG. 3A are illustrated. The media pipeline316 is optional in some embodiments of the GPE 410 and may not beexplicitly included within the GPE 410. For example and in at least oneembodiment, a separate media and/or image processor is coupled to theGPE 410.

In some embodiments, GPE 410 couples with or includes a command streamer403, which provides a command stream to the 3D pipeline 312 and/or mediapipelines 316. In some embodiments, command streamer 403 is coupled withmemory, which can be system memory, or one or more of internal cachememory and shared cache memory. In some embodiments, command streamer403 receives commands from the memory and sends the commands to 3Dpipeline 312 and/or media pipeline 316. The commands are directivesfetched from a ring buffer, which stores commands for the 3D pipeline312 and media pipeline 316. In one embodiment, the ring buffer canadditionally include batch command buffers storing batches of multiplecommands. The commands for the 3D pipeline 312 can also includereferences to data stored in memory, such as but not limited to vertexand geometry data for the 3D pipeline 312 and/or image data and memoryobjects for the media pipeline 316. The 3D pipeline 312 and mediapipeline 316 process the commands and data by performing operations vialogic within the respective pipelines or by dispatching one or moreexecution threads to a graphics core array 414. In one embodiment thegraphics core array 414 include one or more blocks of graphics cores(e.g., graphics core(s) 415A, graphics core(s) 4158), each blockincluding one or more graphics cores. Each graphics core includes a setof graphics execution resources that includes general-purpose andgraphics specific execution logic to perform graphics and computeoperations, as well as fixed function texture processing and/or machinelearning and artificial intelligence acceleration logic.

In various embodiments the 3D pipeline 312 can include fixed functionand programmable logic to process one or more shader programs, such asvertex shaders, geometry shaders, pixel shaders, fragment shaders,compute shaders, or other shader programs, by processing theinstructions and dispatching execution threads to the graphics corearray 414. The graphics core array 414 provides a unified block ofexecution resources for use in processing these shader programs.Multi-purpose execution logic (e.g., execution units) within thegraphics core(s) 415A-414B of the graphic core array 414 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 414 includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units include general-purpose logicthat is programmable to perform parallel general-purpose computationaloperations, in addition to graphics processing operations. Thegeneral-purpose logic can perform processing operations in parallel orin conjunction with general-purpose logic within the processor core(s)107 of FIG. 1 or core 202A-202N as in FIG. 2A.

Output data generated by threads executing on the graphics core array414 can output data to memory in a unified return buffer (URB) 418. TheURB 418 can store data for multiple threads. In some embodiments the URB418 may be used to send data between different threads executing on thegraphics core array 414. In some embodiments the URB 418 mayadditionally be used for synchronization between threads on the graphicscore array and fixed function logic within the shared function logic420.

In some embodiments, graphics core array 414 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 410. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 414 couples with shared function logic 420 thatincludes multiple resources that are shared between the graphics coresin the graphics core array. The shared functions within the sharedfunction logic 420 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 414. In variousembodiments, shared function logic 420 includes but is not limited tosampler 421, math 422, and inter-thread communication (ITC) 423 logic.Additionally, some embodiments implement one or more cache(s) 425 withinthe shared function logic 420.

A shared function is implemented at least in a case where the demand fora given specialized function is insufficient for inclusion within thegraphics core array 414. Instead a single instantiation of thatspecialized function is implemented as a stand-alone entity in theshared function logic 420 and shared among the execution resourceswithin the graphics core array 414. The precise set of functions thatare shared between the graphics core array 414 and included within thegraphics core array 414 varies across embodiments. In some embodiments,specific shared functions within the shared function logic 420 that areused extensively by the graphics core array 414 may be included withinshared function logic 416 within the graphics core array 414. In variousembodiments, the shared function logic 416 within the graphics corearray 414 can include some or all logic within the shared function logic420. In one embodiment, all logic elements within the shared functionlogic 420 may be duplicated within the shared function logic 416 of thegraphics core array 414. In one embodiment the shared function logic 420is excluded in favor of the shared function logic 416 within thegraphics core array 414.

Execution Units

FIGS. 5A-5B illustrate thread execution logic 500 including an array ofprocessing elements employed in a graphics processor core according toembodiments described herein. Elements of FIGS. 5A-5B having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. FIG. 5A-5B illustrates anoverview of thread execution logic 500, which may be representative ofhardware logic illustrated with each sub-core 221A-221F of FIG. 2B. FIG.5A is representative of an execution unit within a general-purposegraphics processor, while FIG. 5B is representative of an execution unitthat may be used within a compute accelerator.

As illustrated in FIG. 5A, in some embodiments thread execution logic500 includes a shader processor 502, a thread dispatcher 504,instruction cache 506, a scalable execution unit array including aplurality of execution units 508A-508N, a sampler 510, shared localmemory 511, a data cache 512, and a data port 514. In one embodiment thescalable execution unit array can dynamically scale by enabling ordisabling one or more execution units (e.g., any of execution units508A, 508B, 508C, 508D, through 508N-1 and 508N) based on thecomputational requirements of a workload. In one embodiment the includedcomponents are interconnected via an interconnect fabric that links toeach of the components. In some embodiments, thread execution logic 500includes one or more connections to memory, such as system memory orcache memory, through one or more of instruction cache 506, data port514, sampler 510, and execution units 508A-508N. In some embodiments,each execution unit (e.g. 508A) is a stand-alone programmablegeneral-purpose computational unit that is capable of executing multiplesimultaneous hardware threads while processing multiple data elements inparallel for each thread. In various embodiments, the array of executionunits 508A-508N is scalable to include any number individual executionunits.

In some embodiments, the execution units 508A-508N are primarily used toexecute shader programs. A shader processor 502 can process the variousshader programs and dispatch execution threads associated with theshader programs via a thread dispatcher 504. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units 508A-508N.For example, a geometry pipeline can dispatch vertex, tessellation, orgeometry shaders to the thread execution logic for processing. In someembodiments, thread dispatcher 504 can also process runtime threadspawning requests from the executing shader programs.

In some embodiments, the execution units 508A-508N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 508A-508N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units508A-508N causes a waiting thread to sleep until the requested data hasbeen returned. While the waiting thread is sleeping, hardware resourcesmay be devoted to processing other threads. For example, during a delayassociated with a vertex shader operation, an execution unit can performoperations for a pixel shader, fragment shader, or another type ofshader program, including a different vertex shader. Various embodimentscan apply to use execution by use of Single Instruction Multiple Thread(SIMT) as an alternate to use of SIMD or in addition to use of SIMD.Reference to a SIMD core or operation can apply also to SIMT or apply toSIMD in combination with SIMT.

Each execution unit in execution units 508A-508N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 508A-508N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 54-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

In one embodiment one or more execution units can be combined into afused execution unit 509A-509N having thread control logic (507A-507N)that is common to the fused EUs. Multiple EUs can be fused into an EUgroup. Each EU in the fused EU group can be configured to execute aseparate SIMD hardware thread. The number of EUs in a fused EU group canvary according to embodiments. Additionally, various SIMD widths can beperformed per-EU, including but not limited to SIMD8, SIMD16, andSIMD32. Each fused graphics execution unit 509A-509N includes at leasttwo execution units. For example, fused execution unit 509A includes afirst EU 508A, second EU 508B, and thread control logic 507A that iscommon to the first EU 508A and the second EU 508B. The thread controllogic 507A controls threads executed on the fused graphics executionunit 509A, allowing each EU within the fused execution units 509A-509Nto execute using a common instruction pointer register.

One or more internal instruction caches (e.g., 506) are included in thethread execution logic 500 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,512) are included to cache thread data during thread execution. Threadsexecuting on the execution logic 500 can also store explicitly manageddata in the shared local memory 511. In some embodiments, a sampler 510is included to provide texture sampling for 3D operations and mediasampling for media operations. In some embodiments, sampler 510 includesspecialized texture or media sampling functionality to process textureor media data during the sampling process before providing the sampleddata to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 500 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor502 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 502 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 502dispatches threads to an execution unit (e.g., 508A) via threaddispatcher 504. In some embodiments, shader processor 502 uses texturesampling logic in the sampler 510 to access texture data in texture mapsstored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 514 provides a memory accessmechanism for the thread execution logic 500 to output processed data tomemory for further processing on a graphics processor output pipeline.In some embodiments, the data port 514 includes or couples to one ormore cache memories (e.g., data cache 512) to cache data for memoryaccess via the data port.

In one embodiment, the execution logic 500 can also include a ray tracer505 that can provide ray tracing acceleration functionality. The raytracer 505 can support a ray tracing instruction set that includesinstructions/functions for ray generation. The ray tracing instructionset can be similar to or different from the ray-tracing instruction setsupported by the ray tracing cores 245 in FIG. 2C.

FIG. 5B illustrates exemplary internal details of an execution unit 508,according to embodiments. A graphics execution unit 508 can include aninstruction fetch unit 537, a general register file array (GRF) 524, anarchitectural register file array (ARF) 526, a thread arbiter 522, asend unit 530, a branch unit 532, a set of SIMD floating point units(FPUs) 534, and in one embodiment a set of dedicated integer SIMD ALUs535. The GRF 524 and ARF 526 includes the set of general register filesand architecture register files associated with each simultaneoushardware thread that may be active in the graphics execution unit 508.In one embodiment, per thread architectural state is maintained in theARF 526, while data used during thread execution is stored in the GRF524. The execution state of each thread, including the instructionpointers for each thread, can be held in thread-specific registers inthe ARF 526.

In one embodiment the graphics execution unit 508 has an architecturethat is a combination of Simultaneous Multi-Threading (SMT) andfine-grained Interleaved Multi-Threading (IMT). The architecture has amodular configuration that can be fine-tuned at design time based on atarget number of simultaneous threads and number of registers perexecution unit, where execution unit resources are divided across logicused to execute multiple simultaneous threads. The number of logicalthreads that may be executed by the graphics execution unit 508 is notlimited to the number of hardware threads, and multiple logical threadscan be assigned to each hardware thread.

In one embodiment, the graphics execution unit 508 can co-issue multipleinstructions, which may each be different instructions. The threadarbiter 522 of the graphics execution unit thread 508 can dispatch theinstructions to one of the send unit 530, branch unit 532, or SIMDFPU(s) 534 for execution. Each execution thread can access 128general-purpose registers within the GRF 524, where each register canstore 32 bytes, accessible as a SIMD 8-element vector of 32-bit dataelements. In one embodiment, each execution unit thread has access to 4Kbytes within the GRF 524, although embodiments are not so limited, andgreater or fewer register resources may be provided in otherembodiments. In one embodiment the graphics execution unit 508 ispartitioned into seven hardware threads that can independently performcomputational operations, although the number of threads per executionunit can also vary according to embodiments. For example, in oneembodiment up to 16 hardware threads are supported. In an embodiment inwhich seven threads may access 4 Kbytes, the GRF 524 can store a totalof 28 Kbytes. Where 16 threads may access 4 Kbytes, the GRF 524 canstore a total of 64 Kbytes. Flexible addressing modes can permitregisters to be addressed together to build effectively wider registersor to represent strided rectangular block data structures.

In one embodiment, memory operations, sampler operations, and otherlonger-latency system communications are dispatched via “send”instructions that are executed by the message passing send unit 530. Inone embodiment, branch instructions are dispatched to a dedicated branchunit 532 to facilitate SIMD divergence and eventual convergence.

In one embodiment the graphics execution unit 508 includes one or moreSIMD floating point units (FPU(s)) 534 to perform floating-pointoperations. In one embodiment, the FPU(s) 534 also support integercomputation. In one embodiment the FPU(s) 534 can SIMD execute up to Mnumber of 32-bit floating-point (or integer) operations, or SIMD executeup to 2M 16-bit integer or 16-bit floating-point operations. In oneembodiment, at least one of the FPU(s) provides extended math capabilityto support high-throughput transcendental math functions and doubleprecision 54-bit floating-point. In some embodiments, a set of 8-bitinteger SIMD ALUs 535 are also present, and may be specificallyoptimized to perform operations associated with machine learningcomputations.

In one embodiment, arrays of multiple instances of the graphicsexecution unit 508 can be instantiated in a graphics sub-core grouping(e.g., a sub-slice). For scalability, product architects can choose theexact number of execution units per sub-core grouping. In one embodimentthe execution unit 508 can execute instructions across a plurality ofexecution channels. In a further embodiment, each thread executed on thegraphics execution unit 508 is executed on a different channel.

FIG. 6 illustrates an additional execution unit 600, according to anembodiment. The execution unit 600 may be a compute-optimized executionunit for use in, for example, a compute engine tile 340A-340D as in FIG.3C, but is not limited as such. Variants of the execution unit 600 mayalso be used in a graphics engine tile 310A-310D as in FIG. 3B. In oneembodiment, the execution unit 600 includes a thread control unit 601, athread state unit 602, an instruction fetch/prefetch unit 603, and aninstruction decode unit 604. The execution unit 600 additionallyincludes a register file 606 that stores registers that can be assignedto hardware threads within the execution unit. The execution unit 600additionally includes a send unit 607 and a branch unit 608. In oneembodiment, the send unit 607 and branch unit 608 can operate similarlyas the send unit 530 and a branch unit 532 of the graphics executionunit 508 of FIG. 5B.

The execution unit 600 also includes a compute unit 610 that includesmultiple different types of functional units. In one embodiment thecompute unit 610 includes an ALU unit 611 that includes an array ofarithmetic logic units. The ALU unit 611 can be configured to perform64-bit, 32-bit, and 16-bit integer and floating point operations.Integer and floating point operations may be performed simultaneously.The compute unit 610 can also include a systolic array 612, and a mathunit 613. The systolic array 612 includes a W wide and D deep network ofdata processing units that can be used to perform vector or otherdata-parallel operations in a systolic manner. In one embodiment thesystolic array 612 can be configured to perform matrix operations, suchas matrix dot product operations. In one embodiment the systolic array612 support 16-bit floating point operations, as well as 8-bit and 4-bitinteger operations. In one embodiment the systolic array 612 can beconfigured to accelerate machine learning operations. In suchembodiments, the systolic array 612 can be configured with support forthe bfloat 16-bit floating point format. In one embodiment, a math unit613 can be included to perform a specific subset of mathematicaloperations in an efficient and lower-power manner than then ALU unit611. The math unit 613 can include a variant of math logic that may befound in shared function logic of a graphics processing engine providedby other embodiments (e.g., math logic 422 of the shared function logic420 of FIG. 4 ). In one embodiment the math unit 613 can be configuredto perform 32-bit and 64-bit floating point operations.

The thread control unit 601 includes logic to control the execution ofthreads within the execution unit. The thread control unit 601 caninclude thread arbitration logic to start, stop, and preempt executionof threads within the execution unit 600. The thread state unit 602 canbe used to store thread state for threads assigned to execute on theexecution unit 600. Storing the thread state within the execution unit600 enables the rapid pre-emption of threads when those threads becomeblocked or idle. The instruction fetch/prefetch unit 603 can fetchinstructions from an instruction cache of higher level execution logic(e.g., instruction cache 506 as in FIG. 5A). The instructionfetch/prefetch unit 603 can also issue prefetch requests forinstructions to be loaded into the instruction cache based on ananalysis of currently executing threads. The instruction decode unit 604can be used to decode instructions to be executed by the compute units.In one embodiment, the instruction decode unit 604 can be used as asecondary decoder to decode complex instructions into constituentmicro-operations.

The execution unit 600 additionally includes a register file 606 thatcan be used by hardware threads executing on the execution unit 600.Registers in the register file 606 can be divided across the logic usedto execute multiple simultaneous threads within the compute unit 610 ofthe execution unit 600. The number of logical threads that may beexecuted by the graphics execution unit 600 is not limited to the numberof hardware threads, and multiple logical threads can be assigned toeach hardware thread. The size of the register file 606 can vary acrossembodiments based on the number of supported hardware threads. In oneembodiment, register renaming may be used to dynamically allocateregisters to hardware threads.

FIG. 7 is a block diagram illustrating a graphics processor instructionformats 700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 700 described and illustrated are macro-instructions,in that they are instructions supplied to the execution unit, as opposedto micro-operations resulting from instruction decode once theinstruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 710. A 64-bitcompacted instruction format 730 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 730. The native instructions availablein the 64-bit format 730 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 713. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format710. Other sizes and formats of instruction can be used.

For each format, instruction opcode 712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 710 an exec-size field716 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 716 is not available foruse in the 64-bit compact instruction format 730.

Some execution unit instructions have up to three operands including twosource operands, src0 720, src1 722, and one destination 718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 724), where the instructionopcode 712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726, which specifies an address mode and/or anaccess mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 712bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes amix of instructions, including synchronization instructions (e.g., wait,send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instructiongroup 748 includes component-wise arithmetic instructions (e.g., add,multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel mathgroup 748 performs the arithmetic operations in parallel across datachannels. The vector math group 750 includes arithmetic instructions(e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math groupperforms arithmetic such as dot product calculations on vector operands.The illustrated opcode decode 740, in one embodiment, can be used todetermine which portion of an execution unit will be used to execute adecoded instruction. For example, some instructions may be designated assystolic instructions that will be performed by a systolic array. Otherinstructions, such as ray-tracing instructions (not shown) can be routedto a ray-tracing core or ray-tracing logic within a slice or partitionof execution logic.

Graphics Pipeline

FIG. 8 is a block diagram of another embodiment of a graphics processor800. Elements of FIG. 8 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 800 includes a geometry pipeline820, a media pipeline 830, a display engine 840, thread execution logic850, and a render output pipeline 870. In some embodiments, graphicsprocessor 800 is a graphics processor within a multi-core processingsystem that includes one or more general-purpose processing cores. Thegraphics processor is controlled by register writes to one or morecontrol registers (not shown) or via commands issued to graphicsprocessor 800 via a ring interconnect 802. In some embodiments, ringinterconnect 802 couples graphics processor 800 to other processingcomponents, such as other graphics processors or general-purposeprocessors. Commands from ring interconnect 802 are interpreted by acommand streamer 803, which supplies instructions to individualcomponents of the geometry pipeline 820 or the media pipeline 830.

In some embodiments, command streamer 803 directs the operation of avertex fetcher 805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 803. In someembodiments, vertex fetcher 805 provides vertex data to a vertex shader807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 805 andvertex shader 807 execute vertex-processing instructions by dispatchingexecution threads to execution units 852A-852B via a thread dispatcher831.

In some embodiments, execution units 852A-852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 852A-852B have anattached L1 cache 851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, geometry pipeline 820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 813 operatesat the direction of hull shader 811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to geometry pipeline 820. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 811, tessellator 813, and domain shader 817) can bebypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 819 via one or more threads dispatched to executionunits 852A-852B, or can proceed directly to the clipper 829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader819 receives input from the vertex shader 807. In some embodiments,geometry shader 819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 829 processes vertex data. The clipper829 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 873 in the render output pipeline870 dispatches pixel shaders to convert the geometric objects into perpixel representations. In some embodiments, pixel shader logic isincluded in thread execution logic 850. In some embodiments, anapplication can bypass the rasterizer and depth test component 873 andaccess un-rasterized vertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric,or some other interconnect mechanism that allows data and messagepassing amongst the major components of the processor. In someembodiments, execution units 852A-852B and associated logic units (e.g.,L1 cache 851, sampler 854, texture cache 858, etc.) interconnect via adata port 856 to perform memory access and communicate with renderoutput pipeline components of the processor. In some embodiments,sampler 854, caches 851, 858 and execution units 852A-852B each haveseparate memory access paths. In one embodiment the texture cache 858can also be configured as a sampler cache.

In some embodiments, render output pipeline 870 contains a rasterizerand depth test component 873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache 878and depth cache 879 are also available in some embodiments. A pixeloperations component 877 performs pixel-based operations on the data,though in some instances, pixel operations associated with 2D operations(e.g. bit block image transfers with blending) are performed by the 2Dengine 841, or substituted at display time by the display controller 843using overlay display planes. In some embodiments, a shared L3 cache 875is available to all graphics components, allowing the sharing of datawithout the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes amedia engine 837 and a video front-end 834. In some embodiments, videofront-end 834 receives pipeline commands from the command streamer 803.In some embodiments, media pipeline 830 includes a separate commandstreamer. In some embodiments, video front-end 834 processes mediacommands before sending the command to the media engine 837. In someembodiments, media engine 837 includes thread spawning functionality tospawn threads for dispatch to thread execution logic 850 via threaddispatcher 831.

In some embodiments, graphics processor 800 includes a display engine840. In some embodiments, display engine 840 is external to processor800 and couples with the graphics processor via the ring interconnect802, or some other interconnect bus or fabric. In some embodiments,display engine 840 includes a 2D engine 841 and a display controller843. In some embodiments, display engine 840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, the geometry pipeline 820 and media pipeline 830are configurable to perform operations based on multiple graphics andmedia programming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 9A is a block diagram illustrating a graphics processor commandformat 900 according to some embodiments. FIG. 9B is a block diagramillustrating a graphics processor command sequence 910 according to anembodiment. The solid lined boxes in FIG. 9A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 900 of FIG. 9A includes data fields to identify a client902, a command operation code (opcode) 904, and data 906 for thecommand. A sub-opcode 905 and a command size 908 are also included insome commands.

In some embodiments, client 902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 904 and, if present, sub-opcode 905 to determine theoperation to perform. The client unit performs the command usinginformation in data field 906. For some commands an explicit commandsize 908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word. Othercommand formats can be used.

The flow diagram in FIG. 9B illustrates an exemplary graphics processorcommand sequence 910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 maybegin with a pipeline flush command 912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 922 and the media pipeline 924 do notoperate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 913 isrequired only once within an execution context before issuing pipelinecommands unless the context is to issue commands for both pipelines. Insome embodiments, a pipeline flush command 912 is required immediatelybefore a pipeline switch via the pipeline select command 913.

In some embodiments, a pipeline control command 914 configures agraphics pipeline for operation and is used to program the 3D pipeline922 and the media pipeline 924. In some embodiments, pipeline controlcommand 914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 916 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 916 includes selecting the size and number of returnbuffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 920,the command sequence is tailored to the 3D pipeline 922 beginning withthe 3D pipeline state 930 or the media pipeline 924 beginning at themedia pipeline state 940.

The commands to configure the 3D pipeline state 930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 930 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 922 dispatches shader execution threads to graphicsprocessor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 910 followsthe media pipeline 924 path when performing media operations. Ingeneral, the specific use and manner of programming for the mediapipeline 924 depends on the media or compute operations to be performed.Specific media decode operations may be offloaded to the media pipelineduring media decode. In some embodiments, the media pipeline can also bebypassed and media decode can be performed in whole or in part usingresources provided by one or more general-purpose processing cores. Inone embodiment, the media pipeline also includes elements forgeneral-purpose graphics processor unit (GPGPU) operations, where thegraphics processor is used to perform SIMD vector operations usingcomputational shader programs that are not explicitly related to therendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similarmanner as the 3D pipeline 922. A set of commands to configure the mediapipeline state 940 are dispatched or placed into a command queue beforethe media object commands 942. In some embodiments, commands for themedia pipeline state 940 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 940 also support the use of one ormore pointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 942. Once the pipeline state is configured andmedia object commands 942 are queued, the media pipeline 924 istriggered via an execute command 944 or an equivalent execute event(e.g., register write). Output from media pipeline 924 may then be postprocessed by operations provided by the 3D pipeline 922 or the mediapipeline 924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 10 illustrates an exemplary graphics software architecture for adata processing system 1000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1010, an operating system 1020, and at least one processor 1030. In someembodiments, processor 1030 includes a graphics processor 1032 and oneor more general-purpose processor core(s) 1034. The graphics application1010 and operating system 1020 each execute in the system memory 1050 ofthe data processing system.

In some embodiments, 3D graphics application 1010 contains one or moreshader programs including shader instructions 1012. The shader languageinstructions may be in a high-level shader language, such as theHigh-Level Shader Language (HLSL) of Direct3D, the OpenGL ShaderLanguage (GLSL), and so forth. The application also includes executableinstructions 1014 in a machine language suitable for execution by thegeneral-purpose processor core 1034. The application also includesgraphics objects 1016 defined by vertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1020 can support agraphics API 1022 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 1020uses a front-end shader compiler 1024 to compile any shader instructions1012 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 1010. In some embodiments, the shader instructions 1012 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1026 contains a back-endshader compiler 1027 to convert the shader instructions 1012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1012 in the GLSL high-level language are passed to a usermode graphics driver 1026 for compilation. In some embodiments, usermode graphics driver 1026 uses operating system kernel mode functions1028 to communicate with a kernel mode graphics driver 1029. In someembodiments, kernel mode graphics driver 1029 communicates with graphicsprocessor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 11A is a block diagram illustrating an IP core development system1100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system1100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility1130 can generate a software simulation 1110 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation1110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 1112. The simulation model 1112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 1115 can then be created or synthesized from thesimulation model 1112. The RTL design 1115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 1115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by thedesign facility into a hardware model 1120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 1165 using non-volatile memory 1140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 1150 or wireless connection 1160. Thefabrication facility 1165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

FIG. 11B illustrates a cross-section side view of an integrated circuitpackage assembly 1170, according to some embodiments described herein.The integrated circuit package assembly 1170 illustrates animplementation of one or more processor or accelerator devices asdescribed herein. The package assembly 1170 includes multiple units ofhardware logic 1172, 1174 connected to a substrate 1180. The logic 1172,1174 may be implemented at least partly in configurable logic orfixed-functionality logic hardware, and can include one or more portionsof any of the processor core(s), graphics processor(s), or otheraccelerator devices described herein. Each unit of logic 1172, 1174 canbe implemented within a semiconductor die and coupled with the substrate1180 via an interconnect structure 1173. The interconnect structure 1173may be configured to route electrical signals between the logic 1172,1174 and the substrate 1180, and can include interconnects such as, butnot limited to bumps or pillars. In some embodiments, the interconnectstructure 1173 may be configured to route electrical signals such as,for example, input/output (I/O) signals and/or power or ground signalsassociated with the operation of the logic 1172, 1174. In someembodiments, the substrate 1180 is an epoxy-based laminate substrate.The substrate 1180 may include other suitable types of substrates inother embodiments. The package assembly 1170 can be connected to otherelectrical devices via a package interconnect 1183. The packageinterconnect 1183 may be coupled to a surface of the substrate 1180 toroute electrical signals to other electrical devices, such as amotherboard, other chipset, or multi-chip module.

In some embodiments, the units of logic 1172, 1174 are electricallycoupled with a bridge 1182 that is configured to route electricalsignals between the logic 1172, 1174. The bridge 1182 may be a denseinterconnect structure that provides a route for electrical signals. Thebridge 1182 may include a bridge substrate composed of glass or asuitable semiconductor material. Electrical routing features can beformed on the bridge substrate to provide a chip-to-chip connectionbetween the logic 1172, 1174.

Although two units of logic 1172, 1174 and a bridge 1182 areillustrated, embodiments described herein may include more or fewerlogic units on one or more dies. The one or more dies may be connectedby zero or more bridges, as the bridge 1182 may be excluded when thelogic is included on a single die. Alternatively, multiple dies or unitsof logic can be connected by one or more bridges. Additionally, multiplelogic units, dies, and bridges can be connected together in otherpossible configurations, including three-dimensional configurations.

FIG. 11C illustrates a package assembly 1190 that includes multipleunits of hardware logic chiplets connected to a substrate 1180 (e.g.,base die). A graphics processing unit, parallel processor, and/orcompute accelerator as described herein can be composed from diversesilicon chiplets that are separately manufactured. In this context, achiplet is an at least partially packaged integrated circuit thatincludes distinct units of logic that can be assembled with otherchiplets into a larger package. A diverse set of chiplets with differentIP core logic can be assembled into a single device. Additionally thechiplets can be integrated into a base die or base chiplet using activeinterposer technology. The concepts described herein enable theinterconnection and communication between the different forms of IPwithin the GPU. IP cores can be manufactured using different processtechnologies and composed during manufacturing, which avoids thecomplexity of converging multiple IPs, especially on a large SoC withseveral flavors IPs, to the same manufacturing process. Enabling the useof multiple process technologies improves the time to market andprovides a cost-effective way to create multiple product SKUs.Additionally, the disaggregated IPs are more amenable to being powergated independently, components that are not in use on a given workloadcan be powered off, reducing overall power consumption.

The hardware logic chiplets can include special purpose hardware logicchiplets 1172, logic or I/O chiplets 1174, and/or memory chiplets 1175.The hardware logic chiplets 1172 and logic or I/O chiplets 1174 may beimplemented at least partly in configurable logic or fixed-functionalitylogic hardware and can include one or more portions of any of theprocessor core(s), graphics processor(s), parallel processors, or otheraccelerator devices described herein. The memory chiplets 1175 can beDRAM (e.g., GDDR, HBM) memory or cache (SRAM) memory.

Each chiplet can be fabricated as separate semiconductor die and coupledwith the substrate 1180 via an interconnect structure 1173. Theinterconnect structure 1173 may be configured to route electricalsignals between the various chiplets and logic within the substrate1180. The interconnect structure 1173 can include interconnects such as,but not limited to bumps or pillars. In some embodiments, theinterconnect structure 1173 may be configured to route electricalsignals such as, for example, input/output (I/O) signals and/or power orground signals associated with the operation of the logic, I/O andmemory chiplets.

In some embodiments, the substrate 1180 is an epoxy-based laminatesubstrate. The substrate 1180 may include other suitable types ofsubstrates in other embodiments. The package assembly 1190 can beconnected to other electrical devices via a package interconnect 1183.The package interconnect 1183 may be coupled to a surface of thesubstrate 1180 to route electrical signals to other electrical devices,such as a motherboard, other chipset, or multi-chip module.

In some embodiments, a logic or I/O chiplet 1174 and a memory chiplet1175 can be electrically coupled via a bridge 1187 that is configured toroute electrical signals between the logic or I/O chiplet 1174 and amemory chiplet 1175. The bridge 1187 may be a dense interconnectstructure that provides a route for electrical signals. The bridge 1187may include a bridge substrate composed of glass or a suitablesemiconductor material. Electrical routing features can be formed on thebridge substrate to provide a chip-to-chip connection between the logicor I/O chiplet 1174 and a memory chiplet 1175. The bridge 1187 may alsobe referred to as a silicon bridge or an interconnect bridge. Forexample, the bridge 1187, in some embodiments, is an Embedded Multi-dieInterconnect Bridge (EMIB). In some embodiments, the bridge 1187 maysimply be a direct connection from one chiplet to another chiplet.

The substrate 1180 can include hardware components for I/O 1191, cachememory 1192, and other hardware logic 1193. A fabric 1185 can beembedded in the substrate 1180 to enable communication between thevarious logic chiplets and the logic 1191, 1193 within the substrate1180. In one embodiment, the I/O 1191, fabric 1185, cache, bridge, andother hardware logic 1193 can be integrated into a base die that islayered on top of the substrate 1180.

In various embodiments a package assembly 1190 can include fewer orgreater number of components and chiplets that are interconnected by afabric 1185 or one or more bridges 1187. The chiplets within the packageassembly 1190 may be arranged in a 3D or 2.5D arrangement. In general,bridge structures 1187 may be used to facilitate a point to pointinterconnect between, for example, logic or I/O chiplets and memorychiplets. The fabric 1185 can be used to interconnect the various logicand/or I/O chiplets (e.g., chiplets 1172, 1174, 1191, 1193). with otherlogic and/or I/O chiplets. In one embodiment, the cache memory 1192within the substrate can act as a global cache for the package assembly1190, part of a distributed global cache, or as a dedicated cache forthe fabric 1185.

FIG. 11D illustrates a package assembly 1194 including interchangeablechiplets 1195, according to an embodiment. The interchangeable chiplets1195 can be assembled into standardized slots on one or more basechiplets 1196, 1198. The base chiplets 1196, 1198 can be coupled via abridge interconnect 1197, which can be similar to the other bridgeinterconnects described herein and may be, for example, an EMIB. Memorychiplets can also be connected to logic or I/O chiplets via a bridgeinterconnect. I/O and logic chiplets can communicate via an interconnectfabric. The base chiplets can each support one or more slots in astandardized format for one of logic or I/O or memory/cache.

In one embodiment, SRAM and power delivery circuits can be fabricatedinto one or more of the base chiplets 1196, 1198, which can befabricated using a different process technology relative to theinterchangeable chiplets 1195 that are stacked on top of the basechiplets. For example, the base chiplets 1196, 1198 can be fabricatedusing a larger process technology, while the interchangeable chipletscan be manufactured using a smaller process technology. One or more ofthe interchangeable chiplets 1195 may be memory (e.g., DRAM) chiplets.Different memory densities can be selected for the package assembly 1194based on the power, and/or performance targeted for the product thatuses the package assembly 1194. Additionally, logic chiplets with adifferent number of type of functional units can be selected at time ofassembly based on the power, and/or performance targeted for theproduct. Additionally, chiplets containing IP logic cores of differingtypes can be inserted into the interchangeable chiplet slots, enablinghybrid processor designs that can mix and match different technology IPblocks,

Exemplary System on a Chip Integrated Circuit

FIGS. 12-14 illustrate exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general-purpose processor cores.

FIG. 12 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1200 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 1200includes one or more application processor(s) 1205 (e.g., CPUs), atleast one graphics processor 1210, and may additionally include an imageprocessor 1215 and/or a video processor 1220, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 1200 includes peripheral or bus logic including a USBcontroller 1225, UART controller 1230, an SPI/SDIO controller 1235, andan I²S/I²C controller 1240. Additionally, the integrated circuit caninclude a display device 1245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 1250 and a mobileindustry processor interface (MIPI) display interface 1255. Storage maybe provided by a flash memory subsystem 1260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 1265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine1270.

FIGS. 13-14 are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein. FIG. 13 illustrates an exemplary graphics processor 1310 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to an embodiment. FIG. 13 illustrates anadditional exemplary graphics processor 1340 of a system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment. Graphics processor 1310 of FIG. 13A is anexample of a low power graphics processor core. Graphics processor 1340of FIG. 13B is an example of a higher performance graphics processorcore. Each of the graphics processors 1310, 1340 can be variants of thegraphics processor 1210 of FIG. 12 .

As shown in FIG. 13 , graphics processor 1310 includes a vertexprocessor 1305 and one or more fragment processor(s) 1315A-1315N (e.g.,1315A, 1315B, 1315C, 1315D, through 1315N-1, and 1315N). Graphicsprocessor 1310 can execute different shader programs via separate logic,such that the vertex processor 1305 is optimized to execute operationsfor vertex shader programs, while the one or more fragment processor(s)1315A-1315N execute fragment (e.g., pixel) shading operations forfragment or pixel shader programs. The vertex processor 1305 performsthe vertex processing stage of the 3D graphics pipeline and generatesprimitives and vertex data. The fragment processor(s) 1315A-1315N usethe primitive and vertex data generated by the vertex processor 1305 toproduce a framebuffer that is displayed on a display device. In oneembodiment, the fragment processor(s) 1315A-1315N are optimized toexecute fragment shader programs as provided for in the OpenGL API,which may be used to perform similar operations as a pixel shaderprogram as provided for in the Direct 3D API.

Graphics processor 1310 additionally includes one or more memorymanagement units (MMUs) 1320A-1320B, cache(s) 1325A-1325B, and circuitinterconnect(s) 1330A-1330B. The one or more MMU(s) 1320A-1320B providefor virtual to physical address mapping for the graphics processor 1310,including for the vertex processor 1305 and/or fragment processor(s)1315A-1315N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 1325A-1325B. In one embodiment the one or more MMU(s)1320A-1320B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 1205, image processor 1215, and/or video processor 1220 ofFIG. 12 , such that each processor 1205-1220 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 1330A-1330B enable graphics processor 1310 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

As shown FIG. 14 , graphics processor 1340 includes the one or moreMMU(s) 1320A-1320B, cache(s) 1325A-1325B, and circuit interconnect(s)1330A-1330B of the graphics processor 1310 of FIG. 13 . Graphicsprocessor 1340 includes one or more shader core(s) 1355A-1355N (e.g.,1455A, 1355B, 1355C, 1355D, 1355E, 1355F, through 1355N-1, and 1355N),which provides for a unified shader core architecture in which a singlecore or type or core can execute all types of programmable shader code,including shader program code to implement vertex shaders, fragmentshaders, and/or compute shaders. The exact number of shader corespresent can vary among embodiments and implementations. Additionally,graphics processor 1340 includes an inter-core task manager 1345, whichacts as a thread dispatcher to dispatch execution threads to one or moreshader cores 1355A-1355N and a tiling unit 1358 to accelerate tilingoperations for tile-based rendering, in which rendering operations for ascene are subdivided in image space, for example to exploit localspatial coherence within a scene or to optimize use of internal caches.

Ray Tracing with Machine Learning

As mentioned above, ray tracing is a graphics processing technique inwhich a light transport is simulated through physically-based rendering.One of the key operations in ray tracing is processing a visibilityquery which requires traversal and intersection testing of nodes in abounding volume hierarchy (BVH).

Ray- and path-tracing based techniques compute images by tracing raysand paths through each pixel, and using random sampling to computeadvanced effects such as shadows, glossiness, indirect illumination,etc. Using only a few samples is fast but produces noisy images whileusing many samples produces high quality images, but is costprohibitive.

Machine learning includes any circuitry, program code, or combinationthereof capable of progressively improving performance of a specifiedtask or rendering progressively more accurate predictions or decisions.Some machine learning engines can perform these tasks or render thesepredictions/decisions without being explicitly programmed to perform thetasks or render the predictions/decisions. A variety of machine learningtechniques exist including (but not limited to) supervised andsemi-supervised learning, unsupervised learning, and reinforcementlearning.

In the last several years, a breakthrough solution to ray-/path-tracingfor real-time use has come in the form of “denoising”—the process ofusing image processing techniques to produce high quality,filtered/denoised images from noisy, low-sample count inputs. The mosteffective denoising techniques rely on machine learning techniques wherea machine-learning engine learns what a noisy image would likely looklike if it had been computed with more samples. In one particularimplementation, the machine learning is performed by a convolutionalneural network (CNN); however, the underlying principles of theinvention are not limited to a CNN implementation. In such animplementation, training data is produced with low-sample count inputsand ground-truth. The CNN is trained to predict the converged pixel froma neighborhood of noisy pixel inputs around the pixel in question.

Though not perfect, this AI-based denoising technique has provensurprisingly effective. The caveat, however, is that good training datais required, since the network may otherwise predict the wrong results.For example, if an animated movie studio trained a denoising CNN on pastmovies with scenes on land and then attempted to use the trained CNN todenoise frames from a new movie set on water, the denoising operationwill perform sub-optimally.

To address this problem, learning data can be dynamically gathered,while rendering, and a machine learning engine, such as a CNN, may becontinuously trained based on the data on which it is currently beingrun, thus continuously improving the machine learning engine for thetask at hand. Therefore, a training phase may still performed prior toruntime, but continued to adjust the machine learning weights as neededduring runtime. Thereby, the high cost of computing the reference datarequired for the training is avoided by restricting the generation oflearning data to a sub-region of the image every frame or every Nframes. In particular, the noisy inputs of a frame are generated fordenoising the full frame with the current network. In addition, a smallregion of reference pixels are generated and used for continuoustraining, as described below.

While a CNN implementation is described herein, any form of machinelearning engine may be used including, but not limited to systems whichperform supervised learning (e.g., building a mathematical model of aset of data that contains both the inputs and the desired outputs),unsupervised learning (e.g., which evaluate the input data for certaintypes of structure), and/or a combination of supervised and unsupervisedlearning.

Existing de-noising implementations operate in a training phase and aruntime phase. During the training phase, a network topology is definedwhich receives a region of N×N pixels with various per-pixel datachannels such as pixel color, depth, normal, normal deviation, primitiveIDs, and albedo and generates a final pixel color. A set of“representative” training data is generated using one frame's worth oflow-sample count inputs, and referencing the “desired” pixel colorscomputed with a very high sample count. The network is trained towardsthese inputs, generating a set of “ideal” weights for the network. Inthese implementations, the reference data is used to train the network'sweights to most closely match the network's output to the desiredresult.

At runtime, the given, pre-computed ideal network weights are loaded andthe network is initialized. For each frame, a low-sample count image ofdenoising inputs (i.e., the same as used for training) is generated. Foreach pixel, the given neighborhood of pixels' inputs is run through thenetwork to predict the “denoised” pixel color, generating a denoisedframe.

FIG. 15 illustrates an initial training implementation. A machinelearning engine 1500 (e.g., a CNN) receives a region of N×N pixels ashigh sample count image data 1702 with various per-pixel data channelssuch as pixel color, depth, normal, normal deviation, primitive IDs, andalbedo and generates final pixel colors. Representative training data isgenerated using one frame's worth of low-sample count inputs 1501. Thenetwork is trained towards these inputs, generating a set of “ideal”weights 1505 which the machine learning engine 1500 subsequently uses todenoise low sample count images at runtime.

To improve the above techniques, the denoising phase to generate newtraining data every frame or a subset of frames (e.g., every N frameswhere N=2, 3, 4, 10, 25, etc) is augmented. In particular, asillustrated in FIG. 16 , one or more regions in each frame are chosen,referred to here as “new reference regions” 1602 which are rendered witha high sample count into a separate high sample count buffer 1604. A lowsample count buffer 1603 stores the low sample count input frame 1601(including the low sample region 1604 corresponding to the new referenceregion 1602).

The location of the new reference region 1602 may be randomly selected.Alternatively, the location of the new reference region 1602 may beadjusted in a pre-specified manner for each new frame (e.g., using apredefined movement of the region between frames, limited to a specifiedregion in the center of the frame, etc).

Regardless of how the new reference region is selected, it is used bythe machine learning engine 1600 to continually refine and update thetrained weights 1605 used for denoising. In particular, reference pixelcolors from each new reference region 1602 and noisy reference pixelinputs from a corresponding low sample count region 1607 are rendered.Supplemental training is then performed on the machine learning engine1600 using the high-sample-count reference region 1602 and thecorresponding low sample count region 1607. In contrast to the initialtraining, this training is performed continuously during runtime foreach new reference region 1602—thereby ensuring that the machinelearning engine 1600 is precisely trained. For example, per-pixel datachannels (e.g., pixel color, depth, normal, normal deviation, etc) maybe evaluated, which the machine learning engine 1600 uses to makeadjustments to the trained weights 1605. As in the training case (FIG.15 ), the machine learning engine 1600 is trained towards a set of idealweights 1605 for removing noise from the low sample count input frame1601 to generate the denoised frame 1620. However, the trained weights1605 are continually updated, based on new image characteristics of newtypes of low sample count input frames 1601.

The re-training operations performed by the machine learning engine 1600may be executed concurrently in a background process on the graphicsprocessor unit (GPU) or host processor. The render loop, which may beimplemented as a driver component and/or a GPU hardware component, maycontinuously produce new training data (e.g., in the form of newreference regions 1602) which it places in a queue. The backgroundtraining process, executed on the GPU or host processor, maycontinuously read the new training data from this queue, re-trains themachine learning engine 1600, and update it with new weights 1605 atappropriate intervals.

FIG. 17 illustrates an example of one such implementation in which thebackground training process 1700 is implemented by the host CPU 1710. Inparticular, the background training process 1700 uses the high samplecount new reference region 1602 and the corresponding low sample region1604 to continually update the trained weights 1605, thereby updatingthe machine learning engine 1600.

As illustrated in FIG. 18A for the non-limiting example of amulti-player online game, different host machines 1820-1822 individuallygenerate reference regions which a background training process 1700A-Ctransmits to a server 1800 (e.g., such as a gaming server). The server1800 then performs training on a machine learning engine 1810 using thenew reference regions received from each of the hosts 1821-1822,updating the weights 1805 as previously described. It transmits theseweights 1805 to the host machines 1820 which store the weights 1605A-C,thereby updating each individual machine learning engine (not shown).Because the server 1800 may be provided a large number of referenceregions in a short period of time, it can efficiently and preciselyupdate the weights for any given application (e.g., an online game)being executed by the users.

As illustrated in FIG. 18B, the different host machines may generate newtrained weights (e.g., based on training/reference regions 1602 aspreviously described) and share the new trained weights with a server1800 (e.g., such as a gaming server) or, alternatively, use apeer-to-peer sharing protocol. A machine learning management component1810 on the server generates a set of combined weights 1805 using thenew weights received from each of the host machines. The combinedweights 1805, for example, may be an average generated from the newweights and continually updated as described herein. Once generated,copies of the combined weights 1605A-C may be transmitted and stored oneach of the host machines 1820-1821 which may then use the combinedweights as described herein to perform de-noising operations.

The semi-closed loop update mechanism can also be used by the hardwaremanufacturer. For example, the reference network may be included as partof the driver distributed by the hardware manufacturer. As the drivergenerates new training data using the techniques described herein andcontinuously submits these back to the hardware manufacturer, thehardware manufacturer uses this information to continue to improve itsmachine learning implementations for the next driver update.

In an example implementation (e.g., in batch movie rendering on a renderfarm), the renderer transmits the newly generated training regions to adedicated server or database (in that studio's render farm) thataggregates this data from multiple render nodes over time. A separateprocess on a separate machine continuously improves the studio'sdedicated denoising network, and new render jobs always use the latesttrained network.

A machine-learning method is illustrated in FIG. 19 . The method may beimplemented on the architectures described herein, but is not limited toany particular system or graphics processing architecture.

At 1901, as part of the initial training phase, low sample count imagedata and high sample count image data are generated for a plurality ofimage frames. At 1902, a machine-learning denoising engine is trainedusing the high/low sample count image data. For example, a set ofconvolutional neural network weights associated with pixel features maybe updated in accordance with the training. However, anymachine-learning architecture may be used.

At 1903, at runtime, low sample count image frames are generated alongwith at least one reference region having a high sample count. At 1904,the high sample count reference region is used by the machine-learningengine and/or separate training logic (e.g., background training module1700) to continually refine the training of the machine learning engine.For example, the high sample count reference region may be used incombination with a corresponding portion of the low sample count imageto continue to teach the machine learning engine 1904 how to mosteffectively perform denoising. In a CNN implementation, for example,this may involve updating the weights associated with the CNN.

Multiple variations described above may be implemented, such as themanner in which the feedback loop to the machine learning engine isconfigured, the entities which generate the training data, the manner inwhich the training data is fed back to training engine, and how theimproved network is provided to the rendering engines. In addition,while the examples described above perform continuous training using asingle reference region, any number of reference regions may be used.Moreover, as previously mentioned, the reference regions may be ofdifferent sizes, may be used on different numbers of image frames, andmay be positioned in different locations within the image frames usingdifferent techniques (e.g., random, according to a predeterminedpattern, etc).

In addition, while a convolutional neural network (CNN) is described asone example of a machine-learning engine 1600, the underlying principlesof the invention may be implemented using any form of machine learningengine which is capable of continually refining its results using newtraining data. By way of example, and not limitation, other machinelearning implementations include the group method of data handling(GMDH), long short-term memory, deep reservoir computing, deep beliefnetworks, tensor deep stacking networks, and deep predictive codingnetworks, to name a few.

Apparatus and Method for Efficient Distributed Denoising

As described above, denoising has become a critical feature forreal-time ray tracing with smooth, noiseless images. Rendering can bedone across a distributed system on multiple devices, but so far theexisting denoising frameworks all operate on a single instance on asingle machine. If rendering is being done across multiple devices, theymay not have all rendered pixels accessible for computing a denoisedportion of the image.

A distributed denoising algorithm that works with both artificialintelligence (AI) and non-AI based denoising techniques is presented.Regions of the image are either already distributed across nodes from adistributed render operation, or split up and distributed from a singleframebuffer. Ghost regions of neighboring regions needed for computingsufficient denoising are collected from neighboring nodes when needed,and the final resulting tiles are composited into a final image.

Distributed Processing

FIG. 20 illustrates multiple nodes 2021-2023 that perform rendering.While only three nodes are illustrated for simplicity, the underlyingprinciples of the invention are not limited to any particular number ofnodes. In fact, a single node may be used to implement certainembodiments of the invention.

Nodes 2021-2023 each render a portion of an image, resulting in regions2011-2013 in this example. While rectangular regions 2011-2013 are shownin FIG. 20 , regions of any shape may be used and any device can processany number of regions. The regions that are needed by a node to performa sufficiently smooth denoising operation are referred to as ghostregions 2011-2013. In other words, the ghost regions 2001-2003 representthe entirety of data required to perform denoising at a specified levelof quality. Lowering the quality level reduces the size of the ghostregion and therefore the amount of data required and raising the qualitylevel increases the ghost region and corresponding data required.

If a node such as node 2021 does have a local copy of a portion of theghost region 2001 required to denoise its region 2011 at a specifiedlevel of quality, the node will retrieve the required data from one ormore “adjacent” nodes, such as node 2022 which owns a portion of ghostregion 2001 as illustrated. Similarly, if node 2022 does have a localcopy of a portion of ghost region 2002 required to denoise its region2012 at the specified level of quality, node 2022 will retrieve therequired ghost region data 2032 from node 2021. The retrieval may beperformed over a bus, an interconnect, a high speed memory fabric, anetwork (e.g., high speed Ethernet), or may even be an on-chipinterconnect in a multi-core chip capable of distributing rendering workamong a plurality of cores (e.g., used for rendering large images ateither extreme resolutions or time varying). Each node 2021-2023 maycomprise an individual execution unit or specified set of executionunits within a graphics processor.

The specific amount of data to be sent is dependent on the denoisingtechniques being used. Moreover, the data from the ghost region mayinclude any data needed to improve denoising of each respective region.For example, the ghost region data may include image colors/wavelengths,intensity/alpha data, and/or normals. However, the underlying principlesof the invention are not limited to any particular set of ghost regiondata.

Additional Details

For slower networks or interconnects, compression of this data can beutilized using existing general purpose lossless or lossy compression.Examples include, but are not limited to, zlib, gzip, andLempel-Ziv-Markov chain algorithm (LZMA). Further content-specificcompression may be used by noting that the delta in ray hit informationbetween frames can be quite sparse, and only the samples that contributeto that delta need to be sent when the node already has the collecteddeltas from previous frames. These can be selectively pushed to nodesthat collect those samples, i, or node i can request samples from othernodes. Lossless compression is used for certain types of data andprogram code while lossy data is used for other types of data.

FIG. 21 illustrates additional details of the interactions between nodes2021-2022. Each node 2021-2022 includes a ray tracing renderingcircuitry 2081-2082 for rendering the respective image regions 2011-2012and ghost regions 2001-2002. Denoisers 2100-2111 execute denoisingoperations on the regions 2011-2012, respectively, which each node2021-2022 is responsible for rendering and denoising. The denoisers2021-2022, for example, may comprise circuitry, software, or anycombination thereof to generate the denoised regions 2121-2122,respectively. As mentioned, when generating denoised regions thedenoisers 2021-2022 may need to rely on data within a ghost region ownedby a different node (e.g., denoiser 2100 may need data from ghost region2002 owned by node 2022).

Thus, the denoisers 2100-2111 may generate the denoised regions2121-2122 using data from regions 2011-2012 and ghost regions 2001-2002,respectively, at least a portion of which may be received from anothernode. Region data managers 2101-2102 may manage data transfers fromghost regions 2001-2002 as described herein. Compressor/decompressorunits 2131-2132 may perform compression and decompression of the ghostregion data exchanged between the nodes 2021-2022, respectively.

For example, region data manager 2101 of node 2021 may, upon requestfrom node 2022, send data from ghost region 2001 tocompressor/decompressor 2131, which compresses the data to generatecompressed data 2106 which it transmits to node 2022, thereby reducingbandwidth over the interconnect, network, bus, or other datacommunication link. Compressor/decompressor 2132 of node 2022 thendecompresses the compressed data 2106 and denoiser 2111 uses thedecompressed ghost data to generate a higher quality denoised region2012 than would be possible with only data from region 2012. The regiondata manager 2102 may store the decompressed data from ghost region 2001in a cache, memory, register file or other storage to make it availableto the denoiser 2111 when generating the denoised region 2122. A similarset of operations may be performed to provide the data from ghost region2002 to denoiser 2100 on node 2021 which uses the data in combinationwith data from region 2011 to generate a higher quality denoised region2121.

Grab Data or Render

If the connection between devices such as nodes 2021-2022 is slow (i.e.,lower than a threshold latency and/or threshold bandwidth), it may befaster to render ghost regions locally rather than requesting theresults from other devices. This can be determined at run-time bytracking network transaction speeds and linearly extrapolated rendertimes for the ghost region size. In such cases where it is faster torender out the entire ghost region, multiple devices may end uprendering the same portions of the image. The resolution of the renderedportion of the ghost regions may be adjusted based on the variance ofthe base region and the determined degree of blurring.

Load Balancing

Static and/or dynamic load balancing schemes may be used to distributethe processing load among the various nodes 2021-2023. For dynamic loadbalancing, the variance determined by the denoising filter may requireboth more time in denoising but drive the amount of samples used torender a particular region of the scene, with low variance and blurryregions of the image requiring fewer samples. The specific regionsassigned to specific nodes may be adjusted dynamically based on datafrom previous frames or dynamically communicated across devices as theyare rendering so that all devices will have the same amount of work.

FIG. 22 illustrates how a monitor 2201-2202 running on each respectivenode 2021-2022 collects performance metric data including, but notlimited to, the time consumed to transmit data over the networkinterface 2211-2212, the time consumed when denoising a region (with andwithout ghost region data), and the time consumed rendering eachregion/ghost region. The monitors 2201-2202 report these performancemetrics back to a manager or load balancer node 2201, which analyzes thedata to identify the current workload on each node 2021-2022 andpotentially determines a more efficient mode of processing the variousdenoised regions 2121-2122. The manager node 2201 then distributes newworkloads for new regions to the nodes 2021-2022 in accordance with thedetected load. For example, the manager node 2201 may transmit more workto those nodes which are not heavily loaded and/or reallocate work fromthose nodes which are overloaded. In addition, the load balancer node2201 may transmit a reconfiguration command to adjust the specificmanner in which rendering and/or denoising is performed by each of thenodes (some examples of which are described above).

Determining Ghost Regions

The sizes and shapes of the ghost regions 2001-2002 may be determinedbased on the denoising algorithm implemented by the denoisers 2100-2111.Their respective sizes can then be dynamically modified based on thedetected variance of the samples being denoised. The learning algorithmused for AI denoising itself may be used for determining appropriateregion sizes, or in other cases such as a bilateral blur thepredetermined filter width will determine the size of the ghost regions2001-2002. In an exemplary implementation which uses a learningalgorithm, the machine learning engine may be executed on the managernode 2201 and/or portions of the machine learning may be executed oneach of the individual nodes 2021-2023 (see, e.g., FIGS. 18A-B andassociated text above).

Gathering the Final Image

The final image may be generated by gathering the rendered and denoisedregions from each of the nodes 2021-2023, without the need for the ghostregions or normals. In FIG. 22 , for example, the denoised regions2121-2122 are transmitted to regions processor 2280 of the manager node2201 which combines the regions to generate the final denoised image2290, which is then displayed on a display 2290. The region processor2280 may combine the regions using a variety of 2D compositingtechniques. Although illustrated as separate components, the regionprocessor 2280 and denoised image 2290 may be integral to the display2290. The various nodes 2021-2022 may use a direct-send technique totransmit the denoised regions 2121-2122 and potentially using variouslossy or lossless compression of the region data.

AI denoising is still a costly operation and as gaming moves into thecloud. As such, distributing processing of denoising across multiplenodes 2021-2022 may become required for achieving real-time frame ratesfor traditional gaming or virtual reality (VR) which requires higherframe rates. Movie studios also often render in large render farms whichcan be utilized for faster denoising.

An exemplary method for performing distributed rendering and denoisingis illustrated in FIG. 23 . The method may be implemented within thecontext of the system architectures described above, but is not limitedto any particular system architecture.

At 2301, graphics work is dispatched to a plurality of nodes whichperform ray tracing operations to render a region of an image frame.Each node may already have data required to perform the operations inmemory. For example, two or more of the nodes may share a common memoryor the local memories of the nodes may already have stored data fromprior ray tracing operations. Alternatively, or in addition, certaindata may be transmitted to each node.

At 2302, the “ghost region” required for a specified level of denoising(i.e., at an acceptable level of performance) is determined. The ghostregion comprises any data required to perform the specified level ofdenoising, including data owned by one or more other nodes.

At 2303, data related to the ghost regions (or portions thereof) isexchanged between nodes. At 2304 each node performs denoising on itsrespective region (e.g., using the exchanged data) and at 2305 theresults are combined to generate the final denoised image frame.

A manager node or primary node such as shown in FIG. 22 may dispatch thework to the nodes and then combine the work performed by the nodes togenerate the final image frame. A peer-based architecture can be usedwhere the nodes are peers which exchange data to render and denoise thefinal image frame.

The nodes described herein (e.g., nodes 2021-2023) may be graphicsprocessing computing systems interconnected via a high speed network.Alternatively, the nodes may be individual processing elements coupledto a high speed memory fabric. All of the nodes may share a commonvirtual memory space and/or a common physical memory. Alternatively, thenodes may be a combination of CPUs and GPUs. For example, the managernode 2201 described above may be a CPU and/or software executed on theCPU and the nodes 2021-2022 may be GPUs and/or software executed on theGPUs. Various different types of nodes may be used while still complyingwith the underlying principles of the invention.

Example Neural Network Implementations

There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 24 is a generalized diagram of a machine learning software stack2400. A machine learning application 2402 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 2402 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 2402can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 2402 can beenabled via a machine learning framework 2404. The machine learningframework 2404 may be implemented on hardware described herein, such asthe processing system 100 comprising the processors and componentsdescribed herein. The elements described for FIG. 24 having the same orsimilar names as the elements of any other figure herein describe thesame elements as in the other figures, can operate or function in amanner similar to that, can comprise the same components, and can belinked to other entities, as those described elsewhere herein, but arenot limited to such. The machine learning framework 2404 can provide alibrary of machine learning primitives. Machine learning primitives arebasic operations that are commonly performed by machine learningalgorithms. Without the machine learning framework 2404, developers ofmachine learning algorithms would be required to create and optimize themain computational logic associated with the machine learning algorithm,then re-optimize the computational logic as new parallel processors aredeveloped. Instead, the machine learning application can be configuredto perform the necessary computations using the primitives provided bythe machine learning framework 2404. Exemplary primitives include tensorconvolutions, activation functions, and pooling, which are computationaloperations that are performed while training a convolutional neuralnetwork (CNN). The machine learning framework 2404 can also provideprimitives to implement basic linear algebra subprograms performed bymany machine-learning algorithms, such as matrix and vector operations.

The machine learning framework 2404 can process input data received fromthe machine learning application 2402 and generate the appropriate inputto a compute framework 2406. The compute framework 2406 can abstract theunderlying instructions provided to the GPGPU driver 2408 to enable themachine learning framework 2404 to take advantage of hardwareacceleration via the GPGPU hardware 2410 without requiring the machinelearning framework 2404 to have intimate knowledge of the architectureof the GPGPU hardware 2410. Additionally, the compute framework 2406 canenable hardware acceleration for the machine learning framework 2404across a variety of types and generations of the GPGPU hardware 2410.

GPGPU Machine Learning Acceleration

FIG. 25 illustrates a multi-GPU computing system 2500, which may be avariant of the processing system 100. Therefore, the disclosure of anyfeatures in combination with the processing system 100 herein alsodiscloses a corresponding combination with multi-GPU computing system2500, but is not limited to such. The elements of FIG. 25 having thesame or similar names as the elements of any other figure hereindescribe the same elements as in the other figures, can operate orfunction in a manner similar to that, can comprise the same components,and can be linked to other entities, as those described elsewhereherein, but are not limited to such. The multi-GPU computing system 2500can include a processor 2502 coupled to multiple GPGPUs 2506A-D via ahost interface switch 2504. The host interface switch 2504 may forexample be a PCI express switch device that couples the processor 2502to a PCI express bus over which the processor 2502 can communicate withthe set of GPGPUs 2506A-D. Each of the multiple GPGPUs 2506A-D can be aninstance of the GPGPU described above. The GPGPUs 2506A-D caninterconnect via a set of high-speed point to point GPU to GPU links2516. The high-speed GPU to GPU links can connect to each of the GPGPUs2506A-D via a dedicated GPU link. The P2P GPU links 2516 enable directcommunication between each of the GPGPUs 2506A-D without requiringcommunication over the host interface bus to which the processor 2502 isconnected. With GPU-to-GPU traffic directed to the P2P GPU links, thehost interface bus remains available for system memory access or tocommunicate with other instances of the multi-GPU computing system 2500,for example, via one or more network devices. Instead of connecting theGPGPUs 2506A-D to the processor 2502 via the host interface switch 2504,the processor 2502 can include direct support for the P2P GPU links 2516and, thus, connect directly to the GPGPUs 2506A-D.

Machine Learning Neural Network Implementations

The computing architecture described herein can be configured to performthe types of parallel processing that is particularly suited fortraining and deploying neural networks for machine learning. A neuralnetwork can be generalized as a network of functions having a graphrelationship. As is well-known in the art, there are a variety of typesof neural network implementations used in machine learning. Oneexemplary type of neural network is the feedforward network, aspreviously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for compute vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting andthe concepts illustrated can be applied generally to deep neuralnetworks and machine learning techniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIGS. 26-27 illustrate an exemplary convolutional neural network. FIG.26 illustrates various layers within a CNN. As shown in FIG. 26 , anexemplary CNN used to model image processing can receive input 2602describing the red, green, and blue (RGB) components of an input image.The input 2602 can be processed by multiple convolutional layers (e.g.,convolutional layer 2604, convolutional layer 2606). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 2608. Neurons in a fully connected layer havefull connections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 2608 can be used to generate an output result from the network.The activations within the fully connected layers 2608 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations make use of fully connected layers. For example, in someimplementations the convolutional layer 2606 can generate output for theCNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 2608. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 27 illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 2712 of a CNN can beprocessed in three stages of a convolutional layer 2714. The threestages can include a convolution stage 2716, a detector stage 2718, anda pooling stage 2720. The convolution layer 2714 can then output data toa successive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 2716 performs several convolutions in parallelto produce a set of linear activations. The convolution stage 2716 caninclude an affine transformation, which is any transformation that canbe specified as a linear transformation plus a translation. Affinetransformations include rotations, translations, scaling, andcombinations of these transformations. The convolution stage computesthe output of functions (e.g., neurons) that are connected to specificregions in the input, which can be determined as the local regionassociated with the neuron. The neurons compute a dot product betweenthe weights of the neurons and the region in the local input to whichthe neurons are connected. The output from the convolution stage 2716defines a set of linear activations that are processed by successivestages of the convolutional layer 2714.

The linear activations can be processed by a detector stage 2718. In thedetector stage 2718, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asf(x)=max (0,x), such that the activation is thresholded at zero.

The pooling stage 2720 uses a pooling function that replaces the outputof the convolutional layer 2706 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 2720,including max pooling, average pooling, and l2-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 2714 can then be processed bythe next layer 2722. The next layer 2722 can be an additionalconvolutional layer or one of the fully connected layers 2708. Forexample, the first convolutional layer 2704 of FIG. 27 can output to thesecond convolutional layer 2706, while the second convolutional layercan output to a first layer of the fully connected layers 2808.

FIG. 28 illustrates an exemplary recurrent neural network 2800. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 2800 can bedescribed has having an input layer 2802 that receives an input vector,hidden layers 2804 to implement a recurrent function, a feedbackmechanism 2805 to enable a ‘memory’ of previous states, and an outputlayer 2806 to output a result. The RNN 2800 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 2805. For agiven time step, the state of the hidden layers 2804 is defined by theprevious state and the input at the current time step. An initial input(x1) at a first time step can be processed by the hidden layer 2804. Asecond input (x2) can be processed by the hidden layer 2804 using stateinformation that is determined during the processing of the initialinput (x1). A given state can be computed as s_t=f(Ux_t+Ws_(t−1)), whereU and W are parameter matrices. The function f is generally anonlinearity, such as the hyperbolic tangent function (Tan h) or avariant of the rectifier function f(x)=max (0,x). However, the specificmathematical function used in the hidden layers 2804 can vary dependingon the specific implementation details of the RNN 2800.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the long shortterm memory (LSTM) RNN. LSTM RNNs are capable of learning long-termdependencies that may be necessary for processing longer sequences oflanguage. A variant on the CNN is a convolutional deep belief network,which has a structure similar to a CNN and is trained in a mannersimilar to a deep belief network. A deep belief network (DBN) is agenerative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 29 illustrates training and deployment of a deep neural network.Once a given network has been structured for a task the neural networkis trained using a training dataset 2902. Various training frameworks2904 have been developed to enable hardware acceleration of the trainingprocess. For example, the machine learning framework described above maybe configured as a training framework. The training framework 2904 canhook into an untrained neural network 2906 and enable the untrainedneural net to be trained using the parallel processing resourcesdescribed herein to generate a trained neural net 2908.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 2902 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 2904 can adjust to adjust the weights that controlthe untrained neural network 2906. The training framework 2904 canprovide tools to monitor how well the untrained neural network 2906 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural net 2908. The trained neural network 2908 can then bedeployed to implement any number of machine learning operations.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 2902 will include input data without any associatedoutput data. The untrained neural network 2906 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 2907 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset2902 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 2908 to adapt tothe new data 2912 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 30A is a block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes such as the nodes described above to perform supervisedor unsupervised training of a neural network. The distributedcomputational nodes can each include one or more host processors and oneor more of the general-purpose processing nodes, such as ahighly-parallel general-purpose graphics processing unit. Asillustrated, distributed learning can be performed model parallelism3002, data parallelism 3004, or a combination of model and dataparallelism.

In model parallelism 3002, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks.

In data parallelism 3004, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 3006 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thehighly-parallel general-purpose graphics processing unit and/or themulti-GPU computing systems described herein. On the contrary, deployedmachine learning platforms generally include lower power parallelprocessors suitable for use in products such as cameras, autonomousrobots, and autonomous vehicles.

FIG. 30B illustrates an exemplary inferencing system on a chip (SOC)3100 suitable for performing inferencing using a trained model. Theelements of FIG. 30B having the same or similar names as the elements ofany other figure herein describe the same elements as in the otherfigures, can operate or function in a manner similar to that, cancomprise the same components, and can be linked to other entities, asthose described elsewhere herein, but are not limited to such. The SOC3100 can integrate processing components including a media processor3102, a vision processor 3104, a GPGPU 3106 and a multi-core processor3108. The SOC 3100 can additionally include on-chip memory 3105 that canenable a shared on-chip data pool that is accessible by each of theprocessing components. The processing components can be optimized forlow power operation to enable deployment to a variety of machinelearning platforms, including autonomous vehicles and autonomous robots.For example, one implementation of the SOC 3100 can be used as a portionof the main control system for an autonomous vehicle. Where the SOC 3100is configured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 3102 and vision processor 3104 canwork in concert to accelerate computer vision operations. The mediaprocessor 3102 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip-memory 3105. The vision processor 3104 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 3104 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back end model computations areperformed by the GPGPU 3106.

The multi-core processor 3108 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 3102 and the visionprocessor 3104. The multi-core processor 3108 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 3106. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 3108. Such softwarecan directly issue computational workloads to the GPGPU 3106 or thecomputational workloads can be issued to the multi-core processor 3108,which can offload at least a portion of those operations to the GPGPU3106.

The GPGPU 3106 can include processing clusters such as a low powerconfiguration of the processing clusters DPLAB06A-DPLAB06H within thehighly-parallel general-purpose graphics processing unit DPLAB00. Theprocessing clusters within the GPGPU 3106 can support instructions thatare specifically optimized to perform inferencing computations on atrained neural network. For example, the GPGPU 3106 can supportinstructions to perform low precision computations such as 8-bit and4-bit integer vector operations.

Ray Tracing Architecture

In one implementation, the graphics processor includes circuitry and/orprogram code for performing real-time ray tracing. A dedicated set ofray tracing cores may be included in the graphics processor to performthe various ray tracing operations described herein, including raytraversal and/or ray intersection operations. In addition to the raytracing cores, multiple sets of graphics processing cores for performingprogrammable shading operations and multiple sets of tensor cores forperforming matrix operations on tensor data may also be included.

FIG. 31 illustrates an exemplary portion of one such graphics processingunit (GPU) 3105 which includes dedicated sets of graphics processingresources arranged into multi-core groups 3100A-N. The graphicsprocessing unit (GPU) 3105 may be a variant of the graphics processor300, the GPGPU 1340 and/or any other graphics processor describedherein. Therefore, the disclosure of any features for graphicsprocessors also discloses a corresponding combination with the GPU 3105,but is not limited to such. Moreover, the elements of FIG. 31 having thesame or similar names as the elements of any other figure hereindescribe the same elements as in the other figures, can operate orfunction in a manner similar to that, can comprise the same components,and can be linked to other entities, as those described elsewhereherein, but are not limited to such. While the details of only a singlemulti-core group 3100A are provided, it will be appreciated that theother multi-core groups 3100B-N may be equipped with the same or similarsets of graphics processing resources.

As illustrated, a multi-core group 3100A may include a set of graphicscores 3130, a set of tensor cores 3140, and a set of ray tracing cores3150. A scheduler/dispatcher 3110 schedules and dispatches the graphicsthreads for execution on the various cores 3130, 3140, 3150. A set ofregister files 3120 store operand values used by the cores 3130, 3140,3150 when executing the graphics threads. These may include, forexample, integer registers for storing integer values, floating pointregisters for storing floating point values, vector registers forstoring packed data elements (integer and/or floating point dataelements) and tile registers for storing tensor/matrix values. The tileregisters may be implemented as combined sets of vector registers.

One or more Level 1 (L1) caches and texture units 3160 store graphicsdata such as texture data, vertex data, pixel data, ray data, boundingvolume data, etc, locally within each multi-core group 3100A. A Level 2(L2) cache 3180 shared by all or a subset of the multi-core groups3100A-N stores graphics data and/or instructions for multiple concurrentgraphics threads. As illustrated, the L2 cache 3180 may be shared acrossa plurality of multi-core groups 3100A-N. One or more memory controllers3170 couple the GPU 3105 to a memory 3198 which may be a system memory(e.g., DRAM) and/or a dedicated graphics memory (e.g., GDDR6 memory).

Input/output (IO) circuitry 3195 couples the GPU 3105 to one or more IOdevices 3195 such as digital signal processors (DSPs), networkcontrollers, or user input devices. An on-chip interconnect may be usedto couple the I/O devices 3190 to the GPU 3105 and memory 3198. One ormore IO memory management units (IOMMUs) 3170 of the IO circuitry 3195couple the IO devices 3190 directly to the system memory 3198. The IOMMU3170 may manage multiple sets of page tables to map virtual addresses tophysical addresses in system memory 3198. Additionally, the IO devices3190, CPU(s) 3199, and GPU(s) 3105 may share the same virtual addressspace.

The IOMMU 3170 may also support virtualization. In this case, it maymanage a first set of page tables to map guest/graphics virtualaddresses to guest/graphics physical addresses and a second set of pagetables to map the guest/graphics physical addresses to system/hostphysical addresses (e.g., within system memory 3198). The base addressesof each of the first and second sets of page tables may be stored incontrol registers and swapped out on a context switch (e.g., so that thenew context is provided with access to the relevant set of page tables).While not illustrated in FIG. 31 , each of the cores 3130, 3140, 3150and/or multi-core groups 3100A-N may include translation lookasidebuffers (TLBs) to cache guest virtual to guest physical translations,guest physical to host physical translations, and guest virtual to hostphysical translations.

The CPUs 3199, GPUs 3105, and IO devices 3190 can be integrated on asingle semiconductor chip and/or chip package. The illustrated memory3198 may be integrated on the same chip or may be coupled to the memorycontrollers 3170 via an off-chip interface. In one implementation, thememory 3198 comprises GDDR6 memory which shares the same virtual addressspace as other physical system-level memories, although the underlyingprinciples of the invention are not limited to this specificimplementation.

The tensor cores 3140 may include a plurality of execution unitsspecifically designed to perform matrix operations, which are thefundamental compute operation used to perform deep learning operations.For example, simultaneous matrix multiplication operations may be usedfor neural network training and inferencing. The tensor cores 3140 mayperform matrix processing using a variety of operand precisionsincluding single precision floating-point (e.g., 32 bits),half-precision floating point (e.g., 16 bits), integer words (16 bits),bytes (8 bits), and half-bytes (4 bits). A neural network implementationmay also extract features of each rendered scene, potentially combiningdetails from multiple frames, to construct a high-quality final image.

In deep learning implementations, parallel matrix multiplication workmay be scheduled for execution on the tensor cores 3140. The training ofneural networks, in particular, requires a significant number matrix dotproduct operations. In order to process an inner-product formulation ofan N×N×N matrix multiply, the tensor cores 3140 may include at least Ndot-product processing elements. Before the matrix multiply begins, oneentire matrix is loaded into tile registers and at least one column of asecond matrix is loaded each cycle for N cycles. Each cycle, there are Ndot products that are processed.

Matrix elements may be stored at different precisions depending on theparticular implementation, including 16-bit words, 8-bit bytes (e.g.,INT8) and 4-bit half-bytes (e.g., INT4). Different precision modes maybe specified for the tensor cores 3140 to ensure that the most efficientprecision is used for different workloads (e.g., such as inferencingworkloads which can tolerate quantization to bytes and half-bytes).

The ray tracing cores 3150 may be used to accelerate ray tracingoperations for both real-time ray tracing and non-real-time ray tracingimplementations. In particular, the ray tracing cores 3150 may includeray traversal/intersection circuitry for performing ray traversal usingbounding volume hierarchies (BVHs) and identifying intersections betweenrays and primitives enclosed within the BVH volumes. The ray tracingcores 3150 may also include circuitry for performing depth testing andculling (e.g., using a Z buffer or similar arrangement). In oneimplementation, the ray tracing cores 3150 perform traversal andintersection operations in concert with the image denoising techniquesdescribed herein, at least a portion of which may be executed on thetensor cores 3140. For example, the tensor cores 3140 may implement adeep learning neural network to perform denoising of frames generated bythe ray tracing cores 3150. However, the CPU(s) 3199, graphics cores3130, and/or ray tracing cores 3150 may also implement all or a portionof the denoising and/or deep learning algorithms.

In addition, as described above, a distributed approach to denoising maybe employed in which the GPU 3105 is in a computing device coupled toother computing devices over a network or high speed interconnect. Theinterconnected computing devices may additionally share neural networklearning/training data to improve the speed with which the overallsystem learns to perform denoising for different types of image framesand/or different graphics applications.

The ray tracing cores 3150 may process all BVH traversal andray-primitive intersections, saving the graphics cores 3130 from beingoverloaded with thousands of instructions per ray. Each ray tracing core3150 may include a first set of specialized circuitry for performingbounding box tests (e.g., for traversal operations) and a second set ofspecialized circuitry for performing the ray-triangle intersection tests(e.g., intersecting rays which have been traversed). Thus, themulti-core group 3100A can simply launch a ray probe, and the raytracing cores 3150 independently perform ray traversal and intersectionand return hit data (e.g., a hit, no hit, multiple hits, etc) to thethread context. The other cores 3130, 3140 may be freed to perform othergraphics or compute work while the ray tracing cores 3150 perform thetraversal and intersection operations.

Each ray tracing core 3150 may include a traversal unit to perform BVHtesting operations and an intersection unit which performs ray-primitiveintersection tests. The intersection unit may then generate a “hit”, “nohit”, or “multiple hit” response, which it provides to the appropriatethread. During the traversal and intersection operations, the executionresources of the other cores (e.g., graphics cores 3130 and tensor cores3140) may be freed to perform other forms of graphics work.

A hybrid rasterization/ray tracing approach may also be used in whichwork is distributed between the graphics cores 3130 and ray tracingcores 3150.

The ray tracing cores 3150 (and/or other cores 3130, 3140) may includehardware support for a ray tracing instruction set such as Microsoft'sDirectX Ray Tracing (DXR) which includes a DispatchRays command, as wellas ray-generation, closest-hit, any-hit, and miss shaders, which enablethe assignment of unique sets of shaders and textures for each object.Another ray tracing platform which may be supported by the ray tracingcores 3150, graphics cores 3130 and tensor cores 3140 is Vulkan 1.1.85.Note, however, that the underlying principles of the invention are notlimited to any particular ray tracing ISA.

In general, the various cores 3150, 3140, 3130 may support a ray tracinginstruction set that includes instructions/functions for ray generation,closest hit, any hit, ray-primitive intersection, per-primitive andhierarchical bounding box construction, miss, visit, and exceptions.More specifically, ray tracing instructions can be included to performthe following functions:

Ray Generation—Ray generation instructions may be executed for eachpixel, sample, or other user-defined work assignment.

Closest Hit—A closest hit instruction may be executed to locate theclosest intersection point of a ray with primitives within a scene.

Any Hit—An any hit instruction identifies multiple intersections betweena ray and primitives within a scene, potentially to identify a newclosest intersection point.

Intersection—An intersection instruction performs a ray-primitiveintersection test and outputs a result.

Per-primitive Bounding box Construction—This instruction builds abounding box around a given primitive or group of primitives (e.g., whenbuilding a new BVH or other acceleration data structure).

Miss—Indicates that a ray misses all geometry within a scene, orspecified region of a scene.

Visit—Indicates the children volumes a ray will traverse.

Exceptions—Includes various types of exception handlers (e.g., invokedfor various error conditions).

Media processing circuitry 3197 includes fixed function circuitry toencode, decode, and transcode media to, from, or between one or moremedia encoding formats, including, but not limited to Moving PictureExperts Group (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC)formats such as H.264/MPEG-4 AVC, as well as the Society of MotionPicture & Television Engineers (SMPTE) 421M/VC-1, and Joint PhotographicExperts Group (JPEG) formats such as JPEG, and Motion JPEG (MJPEG)formats. To process these formats, one embodiment of the mediaprocessing circuitry includes logic for performing scaling/quantization,intra-frame prediction, inter-frame prediction, motion compensation, andmotion estimation.

A display processor 3193 can include a 2D display engine and a displaycontroller. The display processor 3193 may also include special purposelogic capable of operating independently of the other circuitry of theGPU 3105. The display controller includes an interface to couple to adisplay device (not shown), which may be a system integrated displaydevice, as in a laptop computer, or an external display device attachedvia a display device connector.

Hierarchical Beam Tracing

Bounding volume hierarchies are commonly used to improve the efficiencywith which operations are performed on graphics primitives and othergraphics objects. A BVH is a hierarchical tree structure which is builtbased on a set of geometric objects. At the top of the tree structure isthe root node which encloses all of the geometric objects in a givenscene. The individual geometric objects are wrapped in bounding volumesthat form the leaf nodes of the tree. These nodes are then grouped assmall sets and enclosed within larger bounding volumes. These, in turn,are also grouped and enclosed within other larger bounding volumes in arecursive fashion, eventually resulting in a tree structure with asingle bounding volume, represented by the root node, at the top of thetree. Bounding volume hierarchies are used to efficiently support avariety of operations on sets of geometric objects, such as collisiondetection, primitive culling, and ray traversal/intersection operationsused in ray tracing.

In ray tracing architectures, rays are traversed through a BVH todetermine ray-primitive intersections. For example, if a ray does notpass through the root node of the BVH, then the ray does not intersectany of the primitives enclosed by the BVH and no further processing isrequired for the ray with respect to this set of primitives. If a raypasses through a first child node of the BVH but not the second childnode, then the ray need not be tested against any primitives enclosed bythe second child node. In this manner, a BVH provides an efficientmechanism to test for ray-primitive intersections.

Groups of contiguous rays, referred to as “beams” may be tested againstthe BVH, rather than individual rays. FIG. 32 illustrates an exemplarybeam 3201 outlined by four different rays. Any rays which intersect thepatch 3200 defined by the four rays are considered to be within the samebeam. While the beam 3201 in FIG. 32 is defined by a rectangulararrangement of rays, beams may be defined in various other ways whilestill complying with the underlying principles of the invention (e.g.,circles, ellipses, etc).

FIG. 33 illustrates how a ray tracing engine 3310 of a GPU 3320implements the beam tracing techniques described herein. In particular,ray generation circuitry 3304 generates a plurality of rays for whichtraversal and intersection operations are to be performed. However,rather than performing traversal and intersection operations onindividual rays, traversal and intersection operations are performedusing a hierarchy of beams 3307 generated by beam hierarchy constructioncircuitry 3305. The beam hierarchy is analogous to the bounding volumehierarchy (BVH). For example, FIG. 34 provides an example of a primarybeam 3400 which may be subdivided into a plurality of differentcomponents. In particular, primary beam 3400 may be divided intoquadrants 3401-3404 and each quadrant may itself be divided intosub-quadrants such as sub-quadrants A-D within quadrant 3404. Theprimary beam may be subdivided in a variety of ways. For example, theprimary beam may be divided in half (rather than quadrants) and eachhalf may be divided in half, and so on. Regardless of how thesubdivisions are made, a hierarchical structure is generated in asimilar manner as a BVH, e.g., with a root node representing the primarybeam 3400, a first level of child nodes, each represented by a quadrant3401-3404, second level child nodes for each sub-quadrant A-D, and soon.

Once the beam hierarchy 3307 is constructed, traversal/intersectioncircuitry 3306 may perform traversal/intersection operations using thebeam hierarchy 3307 and the BVH 3308. In particular, it may test thebeam against the BVH and cull portions of the beam which do notintersect any portions of the BVH. Using the data shown in FIG. 34 , forexample, if the sub-beams associated with sub-regions 3402 and 3403 donot intersect with the BVH or a particular branch of the BVH, then theymay be culled with respect to the BVH or the branch. The remainingportions 3401, 3404 may be tested against the BVH by performing adepth-first search or other search algorithm.

A method for ray-tracing is illustrated in FIG. 35 . The method may beimplemented within the context of the graphics processing architecturesdescribed above, but is not limited to any particular architecture.

At 3500 a primary beam is constructed comprising a plurality of rays andat 3501, the beam is subdivided and hierarchical data structuresgenerated to create a beam hierarchy. The operations 3500-3501 may beperformed as a single, integrated operation which constructs a beamhierarchy from a plurality of rays. At 3502, the beam hierarchy is usedwith a BVH to cull rays (from the beam hierarchy) and/ornodes/primitives from the BVH. At 3503, ray-primitive intersections aredetermined for the remaining rays and primitives.

Lossy and Lossless Packet Compression in a Distributed Ray TracingSystem

Ray tracing operations may be distributed across a plurality of computenodes coupled together over a network. FIG. 36 , for example,illustrates a ray tracing cluster 3600 comprising a plurality of raytracing nodes 3610-3613 perform ray tracing operations in parallel,potentially combining the results on one of the nodes. In theillustrated architecture, the ray tracing nodes 3610-3613 arecommunicatively coupled to a client-side ray tracing application 3630via a gateway.

One of the difficulties with a distributed architecture is the largeamount of packetized data that must be transmitted between each of theray tracing nodes 3610-3613. Both lossless compression techniques andlossy compression techniques may be used to reduce the data transmittedbetween the ray tracing nodes 3610-3613.

To implement lossless compression, rather than sending packets filledwith the results of certain types of operations, data or commands aresent which allow the receiving node to reconstruct the results. Forexample, stochastically sampled area lights and ambient occlusion (AO)operations do not necessarily need directions. Consequently, atransmitting node can simply send a random seed which is then used bythe receiving node to perform random sampling. For example, if a sceneis distributed across nodes 3610-3612, to sample light 1 at pointsp1-p3, only the light ID and origins need to be sent to nodes 3610-3612.Each of the nodes may then stochastically sample the lightindependently. The random seed may be generated by the receiving node.Similarly, for primary ray hit points, ambient occlusion (AO) and softshadow sampling can be computed on nodes 3610-3612 without waiting forthe original points for successive frames. Additionally, if it is knownthat a set of rays will go to the same point light source, instructionsmay be sent identifying the light source to the receiving node whichwill apply it to the set of rays. As another example, if there are Nambient occlusion rays transmitted a single point, a command may be sentto generate N samples from this point.

Various additional techniques may be applied for lossy compression. Forexample, a quantization factor may be employed to quantize allcoordinate values associated with the BVH, primitives, and rays. Inaddition, 32-bit floating point values used for data such as BVH nodesand primitives may be converted into 8-bit integer values. In anexemplary implementation, the bounds of ray packets are stored in infull precision but individual ray points P1-P3 are transmitted asindexed offsets to the bounds. Similarly, a plurality of localcoordinate systems may be generated which use 8-bit integer values aslocal coordinates. The location of the origin of each of these localcoordinate systems may be encoded using the full precision (e.g., 32-bitfloating point) values, effectively connecting the global and localcoordinate systems.

The following is an example of lossless compression. An example of a Raydata format used internally in a ray tracing program is as follows:

  struct Ray {  uint32 pixId;  uint32 materialID;  uint32 instanceID; uint64 primitiveID;  uint32 geometryID;  uint32 lightID;  floatorigin[3];  float direction[3];  float t0;  float t;  float time;  floatnormal[3]; //used for geometry intersections  float u;  float v;  floatwavelength;  float phase; //Interferometry  float refractedOffset;//Schlieren-esque  float amplitude;  float weight; };

Instead of sending the raw data for each and every node generated, thisdata can be compressed by grouping values and by creating implicit raysusing applicable metadata where possible.

Bundling and Grouping Ray Data

Flags may be used for common data or masks with modifiers.

struct RayPacket

  {  uint32 size;  uint32 flags;  list<Ray> rays; }For example:

RayPacket.rays=ray_1 to ray_256

Origins are all Shared

All ray data is packed, except only a single origin is stored across allrays. RayPacket.flags is set for RAYPACKET_COMMON_ORIGIN. When RayPacketis unpacked when received, origins are filled in from the single originvalue.

Origins are Shared Only Among Some Rays

All ray data is packed, except for rays that share origins. For eachgroup of unique shared origins, an operator is packed on that identifiesthe operation (shared origins), stores the origin, and masks which raysshare the information. Such an operation can be done on any sharedvalues among nodes such as material IDs, primitive IDs, origin,direction, normals, etc.

  struct RayOperation {  uint8 operationID;  void* value;  uint64 mask;}Sending Implicit Rays

Often times, ray data can be derived on the receiving end with minimalmeta information used to generate it. A very common example isgenerating multiple secondary rays to stochastically sample an area.Instead of the sender generating a secondary ray, sending it, and thereceiver operating on it, the sender can send a command that a ray needsto be generated with any dependent information, and the ray is generatedon the receiving end. In the case where the ray needs to be firstgenerated by the sender to determine which receiver to send it to, theray is generated and the random seed can be sent to regenerate the exactsame ray.

For example, to sample a hit point with 64 shadow rays sampling an arealight source, all 64 rays intersect with regions from the same computeN4. A RayPacket with common origin and normal is created. More datacould be sent if one wished the receiver to shade the resulting pixelcontribution, but for this example let us assume we wish to only returnwhether a ray hits another nodes data. A RayOperation is created for agenerate shadow ray operation, and is assigned the value of the lightIDto be sampled and the random number seed. When N4 receieves the raypacket, it generates the fully filled Ray data by filling in the sharedorigin data to all rays and setting the direction based on the lightIDstochastically sampled with the random number seed to generate the samerays that the original sender generated. When the results are returned,only binary results for every ray need be returned, which can be handedby a mask over the rays.

Sending the original 64 rays in this example would have used 104Bytes*64 rays=6656 Bytes. If the returning rays were sent in their rawform as well, than this is also doubled to 13312 Bytes. Using losslesscompression with only sending the common ray origin, normal, and raygeneration operation with seed and ID, only 29 Bytes are sent with 8Bytes returned for the was intersected mask. This results in a datacompression rate that needs to be sent over the network of ˜360:1. Thisdoes not include overhead to process the message itself, which wouldneed to be identified in some way, but that is left up to theimplementation. Other operations may be done for recomputing ray originand directions from the pixelD for primary rays, recalculating pixelIDsbased on the ranges in the raypacket, and many other possibleimplementations for recomputation of values. Similar operations can beused for any single or group of rays sent, including shadows,reflections, refraction, ambient occlusion, intersections, volumeintersections, shading, bounced reflections in path tracing, etc.

FIG. 37 illustrates additional details for two ray tracing nodes3710-3711 which perform compression and decompression of ray tracingpackets. In particular, when a first ray tracing engine 3730 is ready totransmit data to a second ray tracing engine 3731, ray compressioncircuitry 3720 performs lossy and/or lossless compression of the raytracing data as described herein (e.g., converting 32-bit values to8-bit values, substituting raw data for instructions to reconstruct thedata, etc). The compressed ray packets 3701 are transmitted from networkinterface 3725 to network interface 3726 over a local network (e.g., a10 Gb/s, 100 Gb/s Ethernet network). Ray decompression circuitry thendecompresses the ray packets when appropriate. For example, it mayexecute commands to reconstruct the ray tracing data (e.g., using arandom seed to perform random sampling for lighting operations). Raytracing engine 3731 then uses the received data to perform ray tracingoperations.

In the reverse direction, ray compression circuitry 3741 compresses raydata, network interface 3726 transmits the compressed ray data over thenetwork (e.g., using the techniques described herein), ray decompressioncircuitry 3740 decompresses the ray data when necessary and ray tracingengine 3730 uses the data in ray tracing operations. Althoughillustrated as a separate unit in FIG. 37 , ray decompression circuitry3740-3741 may be integrated within ray tracing engines 3730-3731,respectively. For example, to the extent the compressed ray datacomprises commands to reconstruct the ray data, these commands may beexecuted by each respective ray tracing engine 3730-3731.

As illustrated in FIG. 38 , ray compression circuitry 3720 may includelossy compression circuitry 3801 for performing the lossy compressiontechniques described herein (e.g., converting 32-bit floating pointcoordinates to 8-bit integer coordinates) and lossless compressioncircuitry 3803 for performing the lossless compression techniques (e.g.,transmitting commands and data to allow ray recompression circuitry 3821to reconstruct the data). Ray decompression circuitry 3721 includeslossy decompression circuitry 3802 and lossless decompression circuitry3804 for performing lossless decompression.

Another exemplary method is illustrated in FIG. 39 . The method may beimplemented on the ray tracing architectures or other architecturesdescribed herein but is not limited to any particular architecture.

At 3900, ray data is received which will be transmitted from a first raytracing node to a second ray tracing node. At 3901, lossy compressioncircuitry performs lossy compression on first ray tracing data and, at3902, lossless compression circuitry performs lossless compression onsecond ray tracing data. At 3903, the compressed ray racing data istransmitted to a second ray tracing node. At 3904, lossy/losslessdecompression circuitry performs lossy/lossless decompression of the raytracing data and, at 3905, the second ray tracing node performs raytracing operations sing the decompressed data.

Graphics Processor with Hardware Accelerated Hybrid Ray Tracing

A hybrid rendering pipeline which performs rasterization on graphicscores 3130 and ray tracing operations on the ray tracing cores 3150,graphics cores 3130, and/or CPU 3199 cores, is presented next. Forexample, rasterization and depth testing may be performed on thegraphics cores 3130 in place of the primary ray casting stage. The raytracing cores 3150 may then generate secondary rays for ray reflections,refractions, and shadows. In addition, certain regions of a scene inwhich the ray tracing cores 3150 will perform ray tracing operations(e.g., based on material property thresholds such as high reflectivitylevels) will be selected while other regions of the scene will berendered with rasterization on the graphics cores 3130. This hybridimplementation may be used for real-time ray tracing applications—wherelatency is a critical issue.

The ray traversal architecture described below may, for example, performprogrammable shading and control of ray traversal using existing singleinstruction multiple data (SIMD) and/or single instruction multiplethread (SIMT) graphics processors while accelerating critical functions,such as BVH traversal and/or intersections, using dedicated hardware.SIMD occupancy for incoherent paths may be improved by regroupingspawned shaders at specific points during traversal and before shading.This is achieved using dedicated hardware that sorts shadersdynamically, on-chip. Recursion is managed by splitting a function intocontinuations that execute upon returning and regrouping continuationsbefore execution for improved SIMD occupancy.

Programmable control of ray traversal/intersection is achieved bydecomposing traversal functionality into an inner traversal that can beimplemented as fixed function hardware and an outer traversal thatexecutes on GPU processors and enables programmable control through userdefined traversal shaders. The cost of transferring the traversalcontext between hardware and software is reduced by conservativelytruncating the inner traversal state during the transition between innerand outer traversal.

Programmable control of ray tracing can be expressed through thedifferent shader types listed in Table A below. There can be multipleshaders for each type. For example each material can have a differenthit shader.

TABLE A Shader Type Functionality Primary Launching primary rays HitBidirectional reflectance distribution function (BRDF) sampling,launching secondary rays Any Hit Computing transmittance for alphatextured geometry Miss Computing radiance from a light sourceIntersection Intersecting custom shapes Traversal Instance selection andtransformation Callable A general-purpose function

Recursive ray tracing may be initiated by an API function that commandsthe graphics processor to launch a set of primary shaders orintersection circuitry which can spawn ray-scene intersections forprimary rays. This in turn spawns other shaders such as traversal, hitshaders, or miss shaders. A shader that spawns a child shader can alsoreceive a return value from that child shader. Callable shaders aregeneral-purpose functions that can be directly spawned by another shaderand can also return values to the calling shader.

FIG. 40 illustrates a graphics processing architecture which includesshader execution circuitry 4000 and fixed function circuitry 4010. Thegeneral purpose execution hardware subsystem includes a plurality ofsingle instruction multiple data (SIMD) and/or single instructionsmultiple threads (SIMT) cores/execution units (EUs) 4001 (i.e., eachcore may comprise a plurality of execution units), one or more samplers4002, and a Level 1 (L1) cache 4003 or other form of local memory. Thefixed function hardware subsystem 4010 includes message unit 4004, ascheduler 4007, ray-BVH traversal/intersection circuitry 4005, sortingcircuitry 4008, and a local L1 cache 4006.

In operation, primary dispatcher 4009 dispatches a set of primary raysto the scheduler 4007, which schedules work to shaders executed on theSIMD/SIMT cores/EUs 4001. The SIMD cores/EUs 4001 may be ray tracingcores 3150 and/or graphics cores 3130 described above. Execution of theprimary shaders spawns additional work to be performed (e.g., to beexecuted by one or more child shaders and/or fixed function hardware).The message unit 4004 distributes work spawned by the SIMD cores/EUs4001 to the scheduler 4007, accessing the free stack pool as needed, thesorting circuitry 4008, or the ray-BVH intersection circuitry 4005. Ifthe additional work is sent to the scheduler 4007, it is scheduled forprocessing on the SIMD/SIMT cores/EUs 4001. Prior to scheduling, thesorting circuitry 4008 may sort the rays into groups or bins asdescribed herein (e.g., grouping rays with similar characteristics). Theray-BVH intersection circuitry 4005 performs intersection testing ofrays using BVH volumes. For example, the ray-BVH intersection circuitry4005 may compare ray coordinates with each level of the BVH to identifyvolumes which are intersected by the ray.

Shaders can be referenced using a shader record, a user-allocatedstructure that includes a pointer to the entry function, vendor-specificmetadata, and global arguments to the shader executed by the SIMDcores/EUs 4001. Each executing instance of a shader is associated with acall stack which may be used to store arguments passed between a parentshader and child shader. Call stacks may also store references to thecontinuation functions that are executed when a call returns.

FIG. 41 illustrates an example set of assigned stacks 4101 whichincludes a primary shader stack, a hit shader stack, a traversal shaderstack, a continuation function stack, and a ray-BVH intersection stack(which, as described, may be executed by fixed function hardware 4010).New shader invocations may implement new stacks from a free stack pool4102. The call stacks, e.g. stacks comprised by the set of assignedstacks, may be cached in a local L1 cache 4003, 4006 to reduce thelatency of accesses.

There may be a finite number of call stacks, each with a fixed maximumsize “Sstack” allocated in a contiguous region of memory. Therefore thebase address of a stack can be directly computed from a stack index(SID) as base address=SID*Sstack. Stack IDs may be allocated anddeallocated by the scheduler 4007 when scheduling work to the SIMDcores/EUs 4001.

The primary dispatcher 4009 may comprise a graphics processor commandprocessor which dispatches primary shaders in response to a dispatchcommand from the host (e.g., a CPU). The scheduler 4007 may receivethese dispatch requests and launches a primary shader on a SIMDprocessor thread if it can allocate a stack ID for each SIMD lane. StackIDs may be allocated from the free stack pool 4102 that is initializedat the beginning of the dispatch command.

An executing shader can spawn a child shader by sending a spawn messageto the messaging unit 4004. This command includes the stack IDsassociated with the shader and also includes a pointer to the childshader record for each active SIMD lane. A parent shader can only issuethis message once for an active lane. After sending spawn messages forall relevant lanes, the parent shader may terminate.

A shader executed on the SIMD cores/EUs 4001 can also spawnfixed-function tasks such as ray-BVH intersections using a spawn messagewith a shader record pointer reserved for the fixed-function hardware.As mentioned, the messaging unit 4004 sends spawned ray-BVH intersectionwork to the fixed-function ray-BVH intersection circuitry 4005 andcallable shaders directly to the sorting circuitry 4008. The sortingcircuitry may group the shaders by shader record pointer to derive aSIMD batch with similar characteristics. Accordingly, stack IDs fromdifferent parent shaders can be grouped by the sorting circuitry 4008 inthe same batch. The sorting circuitry 4008 sends grouped batches to thescheduler 4007 which accesses the shader record from graphics memory2511 or the last level cache (LLC) 4020 and launches the shader on aprocessor thread.

Continuations may be treated as callable shaders and may also bereferenced through shader records. When a child shader is spawned andreturns values to the parent shader, a pointer to the continuationshader record may be pushed on the call stack 4101. When a child shaderreturns, the continuation shader record may then be popped from the callstack 4101 and a continuation shader may be spawned. Optionally, spawnedcontinuations may go through the sorting unit similar to callableshaders and get launched on a processor thread.

As illustrated in FIG. 42 , the sorting circuitry 4008 groups spawnedtasks by shader record pointers 4201A, 4201B, 4201 n to create SIMDbatches for shading. The stack IDs or context IDs in a sorted batch canbe grouped from different dispatches and different input SIMD lanes. Agrouping circuitry 4210 may perform the sorting using a contentaddressable memory (CAM) structure 4201 comprising a plurality ofentries with each entry identified with a tag 4201. As mentioned, thetag 4201 may be a corresponding shader record pointer 4201A, 4201B, 4201n. The CAM structure 4201 may store a limited number of tags (e.g. 32,64, 128, etc) each associated with an incomplete SIMD batchcorresponding to a shader record pointer.

For an incoming spawn command, each SIMD lane has a corresponding stackID (shown as 16 context IDs 0-15 in each CAM entry) and a shader recordpointer 4201A-B, . . . n (acting as a tag value). The grouping circuitry4210 may compare the shader record pointer for each lane against thetags 4201 in the CAM structure 4201 to find a matching batch. If amatching batch is found, the stack ID/context ID may be added to thebatch. Otherwise a new entry with a new shader record pointer tag may becreated, possibly evicting an older entry with an incomplete batch.

An executing shader can deallocate the call stack when it is empty bysending a deallocate message to the message unit. The deallocate messageis relayed to the scheduler which returns stack IDs/context IDs foractive SIMD lanes to the free pool.

A hybrid approach for ray traversal operations, using a combination offixed-function ray traversal and software ray traversal, is presented.Consequently, it provides the flexibility of software traversal whilemaintaining the efficiency of fixed-function traversal. FIG. 43 shows anacceleration structure which may be used for hybrid traversal, which isa two-level tree with a single top level BVH 4300 and several bottomlevel BVHs 4301 and 4302. Graphical elements are shown to the right toindicate inner traversal paths 4303, outer traversal paths 4304,traversal nodes 4305, leaf nodes with triangles 4306, and leaf nodeswith custom primitives 4307.

The leaf nodes with triangles 4306 in the top level BVH 4300 canreference triangles, intersection shader records for custom primitivesor traversal shader records. The leaf nodes with triangles 4306 of thebottom level BVHs 4301-4302 can only reference triangles andintersection shader records for custom primitives. The type of referenceis encoded within the leaf node 4306. Inner traversal 4303 refers totraversal within each BVH 4300-4302. Inner traversal operations comprisecomputation of ray-BVH intersections and traversal across the BVHstructures 4300-4302 is known as outer traversal. Inner traversaloperations can be implemented efficiently in fixed function hardwarewhile outer traversal operations can be performed with acceptableperformance with programmable shaders. Consequently, inner traversaloperations may be performed using fixed-function circuitry 4010 andouter traversal operations may be performed using the shader executioncircuitry 4000 including SIMD/SIMT cores/EUs 4001 for executingprogrammable shaders.

Note that the SIMD/SIMT cores/EUs 4001 are sometimes simply referred toherein as “cores,” “SIMD cores,” “EUs,” or “SIMD processors” forsimplicity. Similarly, the ray-BVH traversal/intersection circuitry 4005is sometimes simply referred to as a “traversal unit,”“traversal/intersection unit” or “traversal/intersection circuitry.”When an alternate term is used, the particular name used to designatethe respective circuitry/logic does not alter the underlying functionswhich the circuitry/logic performs, as described herein.

Moreover, while illustrated as a single component in FIG. 40 forpurposes of explanation, the traversal/intersection unit 4005 maycomprise a distinct traversal unit and a separate intersection unit,each of which may be implemented in circuitry and/or logic as describedherein.

When a ray intersects a traversal node during an inner traversal, atraversal shader may be spawned. The sorting circuitry 4008 may groupthese shaders by shader record pointers 4201A-B, n to create a SIMDbatch which is launched by the scheduler 4007 for SIMD execution on thegraphics SIMD cores/EUs 4001. Traversal shaders can modify traversal inseveral ways, enabling a wide range of applications. For example, thetraversal shader can select a BVH at a coarser level of detail (LOD) ortransform the ray to enable rigid body transformations. The traversalshader may then spawn inner traversal for the selected BVH.

Inner traversal computes ray-BVH intersections by traversing the BVH andcomputing ray-box and ray-triangle intersections. Inner traversal isspawned in the same manner as shaders by sending a message to themessaging circuitry 4004 which relays the corresponding spawn message tothe ray-BVH intersection circuitry 4005 which computes ray-BVHintersections.

The stack for inner traversal may be stored locally in thefixed-function circuitry 4010 (e.g., within the L1 cache 4006). When aray intersects a leaf node corresponding to a traversal shader or anintersection shader, inner traversal may be terminated and the innerstack truncated. The truncated stack along with a pointer to the ray andBVH may be written to memory at a location specified by the callingshader and then the corresponding traversal shader or intersectionshader may be spawned. If the ray intersects any triangles during innertraversal, the corresponding hit information may be provided as inputarguments to these shaders as shown in the below code. These spawnedshaders may be grouped by the sorting circuitry 4008 to create SIMDbatches for execution.

  struct HitInfo {  float barycentrics[2];  float tmax;  boolinnerTravComplete;  uint primID;  uint geomID;  ShaderRecord*leafShaderRecord; }

Truncating the inner traversal stack reduces the cost of spilling it tomemory. The approach described in Restart Trail for Stackless BVHTraversal, High Performance Graphics (2010), pp. 107-111, to truncatethe stack to a small number of entries at the top of the stack, a 42-bitrestart trail and a 6-bit depth value may be applied. The restart trailindicates branches that have already been taken inside the BVH and thedepth value indicates the depth of traversal corresponding to the laststack entry. This is sufficient information to resume inner traversal ata later time.

Inner traversal is complete when the inner stack is empty and there nomore BVH nodes to test. In this case an outer stack handler is spawnedthat pops the top of the outer stack and resumes traversal if the outerstack is not empty.

Outer traversal may execute the main traversal state machine and may beimplemented in program code executed by the shader execution circuitry4000. It may spawn an inner traversal query under the followingconditions: (1) when a new ray is spawned by a hit shader or a primaryshader; (2) when a traversal shader selects a BVH for traversal; and (3)when an outer stack handler resumes inner traversal for a BVH.

As illustrated in FIG. 44 , before inner traversal is spawned, space isallocated on the call stack 4405 for the fixed-function circuitry 4010to store the truncated inner stack 4410. Offsets 4403-4404 to the top ofthe call stack and the inner stack are maintained in the traversal state4400 which is also stored in memory 2511. The traversal state 4400 alsoincludes the ray in world space 4401 and object space 4402 as well ashit information for the closest intersecting primitive.

The traversal shader, intersection shader and outer stack handler areall spawned by the ray-BVH intersection circuitry 4005. The traversalshader allocates on the call stack 4405 before initiating a new innertraversal for the second level BVH. The outer stack handler is a shaderthat is responsible for updating the hit information and resuming anypending inner traversal tasks. The outer stack handler is alsoresponsible for spawning hit or miss shaders when traversal is complete.Traversal is complete when there are no pending inner traversal queriesto spawn. When traversal is complete and an intersection is found, a hitshader is spawned; otherwise a miss shader is spawned.

While the hybrid traversal scheme described above uses a two-level BVHhierarchy, an arbitrary number of BVH levels with a corresponding changein the outer traversal implementation may also be implemented.

In addition, while fixed function circuitry 4010 is described above forperforming ray-BVH intersections, other system components may also beimplemented in fixed function circuitry. For example, the outer stackhandler described above may be an internal (not user visible) shaderthat could potentially be implemented in the fixed function BVHtraversal/intersection circuitry 4005. This implementation may be usedto reduce the number of dispatched shader stages and round trips betweenthe fixed function intersection hardware 4005 and the processor.

The examples described herein enable programmable shading and raytraversal control using user-defined functions that can execute withgreater SIMD efficiency on existing and future GPU processors.Programmable control of ray traversal enables several important featuressuch as procedural instancing, stochastic level-of-detail selection,custom primitive intersection and lazy BVH updates.

A programmable, multiple instruction multiple data (MIMD) ray tracingarchitecture which supports speculative execution of hit andintersection shaders is also provided. In particular, the architecturefocuses on reducing the scheduling and communication overhead betweenthe programmable SIMD/SIMT cores/execution units 4001 described abovewith respect to FIG. 40 and fixed-function MIMD traversal/intersectionunits 4005 in a hybrid ray tracing architecture. Multiple speculativeexecution schemes of hit and intersection shaders are described belowthat can be dispatched in a single batch from the traversal hardware,avoiding several traversal and shading round trips. A dedicatedcircuitry to implement these techniques may be used.

The embodiments of the invention are particularly beneficial inuse-cases where the execution of multiple hit or intersection shaders isdesired from a ray traversal query that would impose significantoverhead when implemented without dedicated hardware support. Theseinclude, but are not limited to nearest k-hit query (launch a hit shaderfor the k closest intersections) and multiple programmable intersectionshaders.

The techniques described here may be implemented as extensions to thearchitecture illustrated in FIG. 40 (and described with respect to FIGS.40-44 ). In particular, the present embodiments of the invention buildon this architecture with enhancements to improve the performance of theabove-mentioned use-cases.

A performance limitation of hybrid ray tracing traversal architecturesis the overhead of launching traversal queries from the execution unitsand the overhead of invoking programmable shaders from the ray tracinghardware. When multiple hit or intersection shaders are invoked duringthe traversal of the same ray, this overhead generates “executionroundtrips” between the programmable cores 4001 andtraversal/intersection unit 4005. This also places additional pressureto the sorting unit 4008 which needs to extract SIMD/SIMT coherence fromthe individual shader invocations.

Several aspects of ray tracing require programmable control which can beexpressed through the different shader types listed in TABLE A above(i.e., Primary, Hit, Any Hit, Miss, Intersection, Traversal, andCallable). There can be multiple shaders for each type. For example eachmaterial can have a different hit shader. Some of these shader types aredefined in the current Microsoft® Ray Tracing API.

As a brief review, recursive ray tracing is initiated by an API functionthat commands the GPU to launch a set of primary shaders which can spawnray-scene intersections (implemented in hardware and/or software) forprimary rays. This in turn can spawn other shaders such as traversal,hit or miss shaders. A shader that spawns a child shader can alsoreceive a return value from that shader. Callable shaders aregeneral-purpose functions that can be directly spawned by another shaderand can also return values to the calling shader.

Ray traversal computes ray-scene intersections by traversing andintersecting nodes in a bounding volume hierarchy (BVH). Recent researchhas shown that the efficiency of computing ray-scene intersections canbe improved by over an order of magnitude using techniques that arebetter suited to fixed-function hardware such as reduced-precisionarithmetic, BVH compression, per-ray state machines, dedicatedintersection pipelines and custom caches.

The architecture shown in FIG. 40 comprises such a system where an arrayof SIMD/SIMT cores/execution units 4001 interact with a fixed functionray tracing/intersection unit 4005 to perform programmable ray tracing.Programmable shaders are mapped to SIMD/SIMT threads on the executionunits/cores 4001, where SIMD/SIMT utilization, execution, and datacoherence are critical for optimal performance. Ray queries often breakup coherence for various reasons such as:

-   -   Traversal divergence: The duration of the BVH traversal varies        highly    -   among rays favoring asynchronous ray processing.    -   Execution divergence: Rays spawned from different lanes of the        same SIMD/SIMT thread may result in different shader        invocations.    -   Data access divergence: Rays hitting different surfaces sample        different BVH nodes and primitives and shaders access different        textures, for example. A variety of other scenarios may cause        data access divergence.

The SIMD/SIMT cores/execution units 4001 may be variants ofcores/execution units described herein including graphics core(s)415A-415B, shader cores 1355A-N, graphics cores 3130, graphics executionunit 608, execution units 852A-B, or any other cores/execution unitsdescribed herein. The SIMD/SIMT cores/execution units 4001 may be usedin place of the graphics core(s) 415A-415B, shader cores 1355A-N,graphics cores 3130, graphics execution unit 608, execution units852A-B, or any other cores/execution units described herein. Therefore,the disclosure of any features in combination with the graphics core(s)415A-415B, shader cores 1355A-N, graphics cores 3130, graphics executionunit 608, execution units 852A-B, or any other cores/execution unitsdescribed herein also discloses a corresponding combination with theSIMD/SIMT cores/execution units 4001 of FIG. 40 , but is not limited tosuch.

The fixed-function ray tracing/intersection unit 4005 may overcome thefirst two challenges by processing each ray individually andout-of-order. That, however, breaks up SIMD/SIMT groups. The sortingunit 4008 is hence responsible for forming new, coherent SIMD/SIMTgroups of shader invocations to be dispatched to the execution unitsagain.

It is easy to see the benefits of such an architecture compared to apure software-based ray tracing implementation directly on the SIMD/SIMTprocessors. However, there is an overhead associated with the messagingbetween the SIMD/SIMT cores/execution units 4001 (sometimes simplyreferred to herein as SIMD/SIMT processors or cores/EUs) and the MIMDtraversal/intersection unit 4005. Furthermore, the sorting unit 4008 maynot extract perfect SIMD/SIMT utilization from incoherent shader calls.

Use-cases can be identified where shader invocations can be particularlyfrequent during traversal. Enhancements are described for hybrid MIMDray tracing processors to significantly reduce the overhead ofcommunication between the cores/EUs 4001 and traversal/intersectionunits 4005. This may be particularly beneficial when finding thek-closest intersections and implementation of programmable intersectionshaders. Note, however, that the techniques described here are notlimited to any particular processing scenario.

A summary of the high-level costs of the ray tracing context switchbetween the cores/EUs 4001 and fixed function traversal/intersectionunit 4005 is provided below. Most of the performance overhead is causedby these two context switches every time when the shader invocation isnecessary during single-ray traversal.

Each SIMD/SIMT lane that launches a ray generates a spawn message to thetraversal/intersection unit 4005 associated with a BVH to traverse. Thedata (ray traversal context) is relayed to the traversal/intersectionunit 4005 via the spawn message and (cached) memory. When thetraversal/intersection unit 4005 is ready to assign a new hardwarethread to the spawn message it loads the traversal state and performstraversal on the BVH. There is also a setup cost that needs to beperformed before first traversal step on the BVH.

FIG. 45 illustrates an operational flow of a programmable ray tracingpipeline. The shaded elements including traversal 4502 and intersection4503 may be implemented in fixed function circuitry while the remainingelements may be implemented with programmable cores/execution units.

A primary ray shader 4501 sends work to the traversal circuitry at 4502which traverses the current ray(s) through the BVH (or otheracceleration structure). When a leaf node is reached, the traversalcircuitry calls the intersection circuitry at 4503 which, uponidentifying a ray-triangle intersection, invokes an any hit shader at4504 (which may provide results back to the traversal circuitry asindicated).

Alternatively, the traversal may be terminated prior to reaching a leafnode and a closest hit shader invoked at 4507 (if a hit was recorded) ora miss shader at 4506 (in the event of a miss).

As indicated at 4505, an intersection shader may be invoked if thetraversal circuitry reaches a custom primitive leaf node. A customprimitive may be any non-triangle primitive such as a polygon or apolyhedra (e.g., tetrahedrons, voxels, hexahedrons, wedges, pyramids, orother “unstructured” volume). The intersection shader 4505 identifiesany intersections between the ray and custom primitive to the any hitshader 4504 which implements any hit processing.

When hardware traversal 4502 reaches a programmable stage, thetraversal/intersection unit 4005 may generate a shader dispatch messageto a relevant shader 4505-4507, which corresponds to a single SIMD laneof the execution unit(s) used to execute the shader. Since dispatchesoccur in an arbitrary order of rays, and they are divergent in theprograms called, the sorting unit 4008 may accumulate multiple dispatchcalls to extract coherent SIMD batches. The updated traversal state andthe optional shader arguments may be written into memory 2511 by thetraversal/intersection unit 4005.

In the k-nearest intersection problem, a closest hit shader 4507 isexecuted for the first k intersections. In the conventional way thiswould mean ending ray traversal upon finding the closest intersection,invoking a hit-shader, and spawning a new ray from the hit shader tofind the next closest intersection (with the ray origin offset, so thesame intersection will not occur again). It is easy to see that thisimplementation would require k ray spawns for a single ray. Anotherimplementation operates with any-hit shaders 4504, invoked for allintersections and maintaining a global list of nearest intersections,using an insertion sort operation. The main problem with this approachis that there is no upper bound of any-hit shader invocations.

As mentioned, an intersection shader 4505 may be invoked on non-triangle(custom) primitives. Depending on the result of the intersection testand the traversal state (pending node and primitive intersections), thetraversal of the same ray may continue after the execution of theintersection shader 4505. Therefore finding the closest hit may requireseveral roundtrips to the execution unit.

A focus can also be put on the reduction of SIMD-MIMD context switchesfor intersection shaders 4505 and hit shaders 4504, 4507 through changesto the traversal hardware and the shader scheduling model. First, theray traversal circuitry 4005 defers shader invocations by accumulatingmultiple potential invocations and dispatching them in a larger batch.In addition, certain invocations that turn out to be unnecessary may beculled at this stage. Furthermore, the shader scheduler 4007 mayaggregate multiple shader invocations from the same traversal contextinto a single SIMD batch, which results in a single ray spawn message.In one exemplary implementation, the traversal hardware 4005 suspendsthe traversal thread and waits for the results of multiple shaderinvocations. This mode of operation is referred to herein as“speculative” shader execution because it allows the dispatch ofmultiple shaders, some of which may not be called when using sequentialinvocations.

FIG. 46A illustrates an example in which the traversal operationencounters multiple custom primitives 4650 in a subtree and FIG. 46Billustrates how this can be resolved with three intersection dispatchcycles C1-C3. In particular, the scheduler 4007 may require three cyclesto submit the work to the SIMD processor 4001 and the traversalcircuitry 4005 requires three cycles to provide the results to thesorting unit 4008. The traversal state 4601 required by the traversalcircuitry 4005 may be stored in a memory such as a local cache (e.g., anL1 cache and/or L2 cache).

A. Deferred Ray Tracing Shader Invocations

The manner in which the hardware traversal state 4601 is managed toallow the accumulation of multiple potential intersection or hitinvocations in a list can also be modified. At a given time duringtraversal each entry in the list may be used to generate a shaderinvocation. For example, the k-nearest intersection points can beaccumulated on the traversal hardware 4005 and/or in the traversal state4601 in memory, and hit shaders can be invoked for each element if thetraversal is complete. For hit shaders, multiple potential intersectionsmay be accumulated for a subtree in the BVH.

For the nearest-k use case the benefit of this approach is that insteadof k−1 roundtrips to the SIMD core/EU 4001 and k−1 new ray spawnmessages, all hit shaders are invoked from the same traversal threadduring a single traversal operation on the traversal circuitry 4005. Achallenge for potential implementations is that it is not trivial toguarantee the execution order of hit shaders (the standard “roundtrip”approach guarantees that the hit shader of the closest intersection isexecuted first, etc.). This may be addressed by either thesynchronization of the hit shaders or the relaxation of the ordering.

For the intersection shader use case the traversal circuitry 4005 doesnot know in advance whether a given shader would return a positiveintersection test. However, it is possible to speculatively executemultiple intersection shaders and if at least one returns a positive hitresult, it is merged into the global nearest hit. Specificimplementations need to find an optimal number of deferred intersectiontests to reduce the number of dispatch calls but avoid calling too manyredundant intersection shaders.

B. Aggregate Shader Invocations from the Traversal Circuitry

When dispatching multiple shaders from the same ray spawn on thetraversal circuitry 4005, branches in the flow of the ray traversalalgorithm may be created. This may be problematic for intersectionshaders because the rest of the BVH traversal depend on the result ofall dispatched intersection tests. This means that a synchronizationoperation is necessary to wait for the result of the shader invocations,which can be challenging on asynchronous hardware.

Two points of merging the results of the shader calls may be: the SIMDprocessor 4001, and the traversal circuitry 4005. With respect to theSIMD processor 4001, multiple shaders can synchronize and aggregatetheir results using standard programming models. One relatively simpleway to do this is to use global atomics and aggregate results in ashared data structure in memory, where intersection results of multipleshaders could be stored. Then the last shader can resolve the datastructure and call back the traversal circuitry 4005 to continue thetraversal.

A more efficient approach may also be implemented which limits theexecution of multiple shader invocations to lanes of the same SIMDthread on the SIMD processor 4001. The intersection tests are thenlocally reduced using SIMD/SIMT reduction operations (rather thanrelying on global atomics). This implementation may rely on newcircuitry within the sorting unit 4008 to let a small batch of shaderinvocations stay in the same SIMD batch.

The execution of the traversal thread may further be suspended on thetraversal circuitry 4005. Using the conventional execution model, when ashader is dispatched during traversal, the traversal thread isterminated and the ray traversal state is saved to memory to allow theexecution of other ray spawn commands while the execution units 4001process the shaders. If the traversal thread is merely suspended, thetraversal state does not need to be stored and can wait for each shaderresult separately. This implementation may include circuitry to avoiddeadlocks and provide sufficient hardware utilization.

FIGS. 47-48 illustrate examples of a deferred model which invokes asingle shader invocation on the SIMD cores/execution units 4001 withthree shaders 4701. When preserved, all intersection tests are evaluatedwithin the same SIMD/SIMT group. Consequently, the nearest intersectioncan also be computed on the programmable cores/execution units 4001.

As mentioned, all or a portion of the shader aggregation and/or deferralmay be performed by the traversal/intersection circuitry 4005 and/or thecore/EU scheduler 4007. FIG. 47 illustrates how shaderdeferral/aggregator circuitry 4706 within the scheduler 4007 can deferscheduling of shaders associated with a particular SIMD/SIMT thread/laneuntil a specified triggering event has occurred. Upon detecting thetriggering event, the scheduler 4007 dispatches the multiple aggregatedshaders in a single SIMD/SIMT batch to the cores/EUs 4001.

FIG. 48 illustrates how shader deferral/aggregator circuitry 4805 withinthe traversal/intersection circuitry 4005 can defer scheduling ofshaders associated with a particular SIMD thread/lane until a specifiedtriggering event has occurred. Upon detecting the triggering event, thetraversal/intersection circuitry 4005 submits the aggregated shaders tothe sorting unit 4008 in a single SIMD/SIMT batch.

Note, however, that the shader deferral and aggregation techniques maybe implemented within various other components such as the sorting unit4008 or may be distributed across multiple components. For example, thetraversal/intersection circuitry 4005 may perform a first set of shaderaggregation operations and the scheduler 4007 may perform a second setof shader aggregation operations to ensure that shaders for a SIMDthread are scheduled efficiently on the cores/EUs 4001.

The “triggering event” to cause the aggregated shaders to be dispatchedto the cores/EUs may be a processing event such as a particular numberof accumulated shaders or a minimum latency associated with a particularthread. Alternatively, or in addition, the triggering event may be atemporal event such as a certain duration from the deferral of the firstshader or a particular number of processor cycles. Other variables suchas the current workload on the cores/EUs 4001 and thetraversal/intersection unit 4005 may also be evaluated by the scheduler4007 to determine when to dispatch the SIMD/SIMT batch of shaders.

Different embodiments of the invention may be implemented usingdifferent combinations of the above approaches, based on the particularsystem architecture being used and the requirements of the application.

Ray Tracing Instructions

The ray tracing instructions described below are included in aninstruction set architecture (ISA) supported the CPU 3199 and/or GPU3105. If executed by the CPU, the single instruction multiple data(SIMD) instructions may utilize vector/packed source and destinationregisters to perform the described operations and may be decoded andexecuted by a CPU core. If executed by a GPU 3105, the instructions maybe executed by graphics cores 3130. For example, any of the executionunits (EUs) 4001 described above may execute the instructions.Alternatively, or in addition, the instructions may be executed byexecution circuitry on the ray tracing cores 3150 and/or tensor corestensor cores 3140.

FIG. 49 illustrates an architecture for executing the ray tracinginstructions described below. The illustrated architecture may beintegrated within one or more of the cores 3130, 3140, 3150 describedabove (see, e.g., FIG. 31 and associated text) of may be included in adifferent processor architecture.

In operation, an instruction fetch unit 4903 fetches ray tracinginstructions 4900 from memory 3198 and a decoder 4995 decodes theinstructions. In one implementation the decoder 4995 decodesinstructions to generate executable operations (e.g., microoperations oruops in a microcoded core). Alternatively, some or all of the raytracing instructions 4900 may be executed without decoding and, as sucha decoder 4904 is not required.

In either implementation, a scheduler/dispatcher 4905 schedules anddispatches the instructions (or operations) across a set of functionalunits (FUs) 4910-4912. The illustrated implementation includes a vectorFU 4910 for executing single instruction multiple data (SIMD)instructions which operate concurrently on multiple packed data elementsstored in vector registers 4915 and a scalar FU 4911 for operating onscalar values stored in one or more scalar registers 4916. An optionalray tracing FU 4912 may operate on packed data values stored in thevector registers 4915 and/or scalar values stored in the scalarregisters 4916. In an implementation without a dedicated FU 4912, thevector FU 4910 and possibly the scalar FU 4911 may perform the raytracing instructions described below.

The various FUs 4910-4912 access ray tracing data 4902 (e.g.,traversal/intersection data) needed to execute the ray tracinginstructions 4900 from the vector registers 4915, scalar register 4916and/or the local cache subsystem 4908 (e.g., a L1 cache). The FUs4910-4912 may also perform accesses to memory 3198 via load and storeoperations, and the cache subsystem 4908 may operate independently tocache the data locally.

While the ray tracing instructions may be used to increase performancefor ray traversal/intersection and BVH builds, they may also beapplicable to other areas such as high performance computing (HPC) andgeneral purpose GPU (GPGPU) implementations.

In the below descriptions, the term double word is sometimes abbreviateddw and unsigned byte is abbreviated ub. In addition, the source anddestination registers referred to below (e.g., src0, src1, dest, etc)may refer to vector registers 4915 or in some cases a combination ofvector registers 4915 and scalar registers 4916. Typically, if a sourceor destination value used by an instruction includes packed dataelements (e.g., where a source or destination stores N data elements),vector registers 4915 are used. Other values may use scalar registers4916 or vector registers 4915.

Dequantize

One example of the Dequantize instruction “dequantizes” previouslyquantized values. By way of example, in a ray tracing implementation,certain BVH subtrees may be quantized to reduce storage and bandwidthrequirements. The dequantize instruction may take the form dequantizedest src0 src1 src2 where source register src0 stores N unsigned bytes,source register src1 stores 1 unsigned byte, source register src2 stores1 floating point value, and destination register dest stores N floatingpoint values. All of these registers may be vector registers 4915.Alternatively, src0 and dest may be vector registers 4915 and src 1 andsrc2 may be scalar registers 4916.

The following code sequence defines one particular implementation of thedequantize instruction:

  for (int i = 0; i < SIMD_WIDTH) {  if (execMask[i]) {   dst[i] =src2[i] + ldexp(convert_to_float(src0[i]),src1);  } }In this example, Idexp multiplies a double precision floating pointvalue by a specified integral power of two (i.e., Idexp(x,exp)=x*2^(exp)). In the above code, if the execution mask valueassociated with the current SIMD data element (execMask[i])) is set to1, then the SIMD data element at location i in src0 is converted to afloating point value and multiplied by the integral power of the valuein src1 (2^(src1 value)) and this value is added to the correspondingSIMD data element in src2.

Selective Min or Max

A selective min or max instruction may perform either a min or a maxoperation per lane (i.e., returning the minimum or maximum of a set ofvalues), as indicated by a bit in a bitmask. The bitmask may utilize thevector registers 4915, scalar registers 4916, or a separate set of maskregisters (not shown). The following code sequence defines oneparticular implementation of the min/max instruction: sel_min_max destsrc0 src1 src2, where src0 stores N doublewords, src1 stores Ndoublewords, src2 stores one doubleword, and the destination registerstores N doublewords.

The following code sequence defines one particular implementation of theselective min/max instruction:

  for (int i = 0; i < SIMD_WIDTH) {  if (execMask[i]) {  dst[i] = (1 <<i) & src2 ? min(src0[i],src1[i]) :  max(src0[i],src1[i]);  } }In this example, the value of (1<<i) & src2 (a 1 left-shifted by i ANDedwith src2) is used to select either the minimum of the i^(th) dataelement in src0 and src1 or the maximum of the i^(th) data element insrc0 and src1. The operation is performed for the i^(th) data elementonly if the execution mask value associated with the current SIMD dataelement (execMask[i])) is set to 1.

Shuffle Index Instruction

A shuffle index instruction can copy any set of input lanes to theoutput lanes. For a SIMD width of 32, this instruction can be executedat a lower throughput. This instruction takes the form: shuffle_indexdest src0 src1 <optional flag>, where src0 stores N doublewords, src1stores N unsigned bytes (i.e., the index value), and dest stores Ndoublewords.

The following code sequence defines one particular implementation of theshuffle index instruction:

  for (int i = 0; i < SIMD_WIDTH) {  uint8_t srcLane = src1.index[i]; if (execMask[i]) {   bool invalidLane = srcLane < 0 || srcLane >=SIMD_WIDTH || !execMask[srcLaneMod];   if (FLAG) {    invalidLane |=flag[srcLaneMod];   }   if (invalidLane) {    dst[i] = src0[i];   }  else {    dst[i] = src0[srcLane];   }  } }

In the above code, the index in src1 identifies the current lane. If thei^(th) value in the execution mask is set to 1, then a check isperformed to ensure that the source lane is within the range of 0 to theSIMD width. If so, then flag is set (srcLaneMod) and data element i ofthe destination is set equal to data element i of src0. If the lane iswithin range (i.e., is valid), then the index value from src1 (srcLane0)is used as an index into src0 (dst[i]=src0[srcLane]).

Immediate Shuffle Up/Dn/XOR Instruction

An immediate shuffle instruction may shuffle input data elements/lanesbased on an immediate of the instruction. The immediate may specifyshifting the input lanes by 1, 2, 4, 8, or 16 positions, based on thevalue of the immediate. Optionally, an additional scalar source registercan be specified as a fill value. When the source lane index is invalid,the fill value (if provided) is stored to the data element location inthe destination. If no fill value is provided, the data element locationis set to all 0.

A flag register may be used as a source mask. If the flag bit for asource lane is set to 1, the source lane may be marked as invalid andthe instruction may proceed.

The following are examples of different implementations of the immediateshuffle instruction:

  shuffle_<up/dn/xor>_<1/2/4/8/16> dest src0 <optional src1> <optionalflag> shuffle_<up/dn/xor>_<1/2/4/8/16> dest src0 <optional src1><optional flag>In this implementation, src0 stores N doublewords, src1 stores onedoubleword for the fill value (if present), and dest stores Ndoublewords comprising the result.

The following code sequence defines one particular implementation of theimmediate shuffle instruction:

for (int i = 0; i < SIMD_WIDTH) {   int8_t srcLane;  Switch(SHUFFLE_TYPE) {   case UP:    srcLane = i − SHIFT;    case DN:   srcLane = i + SHIFT;   case XOR:    srcLane = i {circumflex over ( )}SHIFT;   }   if (execMask[i]) {    bool invalidLane = srcLane < 0 | |srcLane >=  SIMD_WIDTH | | !execMask[srcLane];    if (FLAG) {    invalidLane | = flag[srcLane];    }    if (invalidLane) {     if(SRC1)      dst[i] = src1;     else    dst[i] = 0;   }   else {   dst[i] = src0[srcLane];   }  } }

Here the input data elements/lanes are shifted by 1, 2, 4, 8, or 16positions, based on the value of the immediate. The register src1 is anadditional scalar source register which is used as a fill value which isstored to the data element location in the destination when the sourcelane index is invalid. If no fill value is provided and the source laneindex is invalid, the data element location in the destination is set to0s. The flag register (FLAG) is used as a source mask. If the flag bitfor a source lane is set to 1, the source lane is marked as invalid andthe instruction proceeds as described above.

Indirect Shuffle Up/Dn/XOR Instruction

The indirect shuffle instruction has a source operand (src1) thatcontrols the mapping from source lanes to destination lanes. Theindirect shuffle instruction may take the form:

shuffle_<up/dn/xor> dest src0 src1 <optional flag>

where src0 stores N doublewords, src1 stores 1 doubleword, and deststores N doublewords.

The following code sequence defines one particular implementation of theimmediate shuffle instruction:

  for (int i = 0; i < SIMD_WIDTH) {  int8_t srcLane; switch(SHUFFLE_TYPE) {  case UP:   srcLane = i − src1;  case DN:  srcLane = i + src1;  case XOR:   srcLane = i {circumflex over ( )}src1;  }  if (execMask[i]) {   bool invalidLane = srcLane < 0 ||srcLane >= SIMD_WIDTH || !execMask[srcLane];   if (FLAG) {   invalidLane |= flag[srcLane];   }   if (invalidLane) {    dst[i] = 0;  }   else {    dst[i] = src0[srcLane];   }  } }

Thus, the indirect shuffle instruction operates in a similar manner tothe immediate shuffle instruction described above, but the mapping ofsource lanes to destination lanes is controlled by the source registersrc1 rather than the immediate.

Cross Lane Min/Max Instruction

A cross lane minimum/maximum instruction may be supported for float andinteger data types. The cross lane minimum instruction may take the formlane_min dest src0 and the cross lane maximum instruction may take theform lane_max dest src0, where src0 stores N doublewords and dest stores1 doubleword.

By way of example, the following code sequence defines one particularimplementation of the cross lane minimum:

  dst = src[0]; for (int i = 1; i < SIMD_WIDTH) {  if (execMask[i]) {  dst = min(dst, src[i]);  } }In this example, the doubleword value in data element position i of thesource register is compared with the data element in the destinationregister and the minimum of the two values is copied to the destinationregister. The cross lane maximum instruction operates in substantiallythe same manner, the only difference being that the maximum of the dataelement in position i and the destination value is selected.

Cross Lane Min/Max Index Instruction

A cross lane minimum index instruction may take the form lane_min_indexdest src0 and the cross lane maximum index instruction may take the formlane_max_index dest src0, where src0 stores N doublewords and deststores 1 doubleword.

By way of example, the following code sequence defines one particularimplementation of the cross lane minimum index instruction:

  dst_index = 0; tmp = src[0] for (int i = 1; i < SIMD_WIDTH) {  if(src[i] < tmp && execMask[i])  {   tmp = src[i];   dst_index = i;  } }In this example, the destination index is incremented from 0 to SIMDwidth, spanning the destination register. If the execution mask bit isset, then the data element at position i in the source register iscopied to a temporary storage location (tmp) and the destination indexis set to data element position i.

Cross Lane Sorting Network Instruction

A cross-lane sorting network instruction may sort all N input elementsusing an N-wide (stable) sorting network, either in ascending order(sortnet_min) or in descending order (sortnet_max). The min/max versionsof the instruction may take the forms sortnet_min dest src0 andsortnet_max dest src0, respectively. In one implementation, src0 anddest store N doublewords. The min/max sorting is performed on the Ndoublewords of src0, and the ascending ordered elements (for min) ordescending ordered elements (for max) are stored in dest in theirrespective sorted orders. One example of a code sequence defining theinstruction is: dst=apply_N_wide_sorting_network_min/max(src0).

Cross Lane Sorting Network Index Instruction

A cross-lane sorting network index instruction may sort all N inputelements using an N-wide (stable) sorting network but returns thepermute index, either in ascending order (sortnet_min) or in descendingorder (sortnet_max). The min/max versions of the instruction may takethe forms sortnet_min_index dest src0 and sortnet_max_index dest src0where src0 and dest each store N doublewords. One example of a codesequence defining the instruction isdst=apply_N_wide_sorting_network_min/max_index(src0).

A method for executing any of the above instructions is illustrated inFIG. 50 . The method may be implemented on the specific processorarchitectures described above, but is not limited to any particularprocessor or system architecture.

At 5001 instructions of a primary graphics thread are executed onprocessor cores. This may include, for example, any of the coresdescribed above (e.g., graphics cores 3130). When ray tracing work isreached within the primary graphics thread, determined at 5002, the raytracing instructions are offloaded to the ray tracing executioncircuitry which may be in the form of a functional unit (FU) such asdescribed above with respect to FIG. 49 or which may be in a dedicatedray tracing core 3150 as described with respect to FIG. 31 .

At 5003, the ray tracing instructions are decoded are fetched frommemory and, at 5005, the instructions are decoded into executableoperations (e.g., in an embodiment which requires a decoder). At 5004the ray tracing instructions are scheduled and dispatched for executionby ray tracing circuitry. At 5005 the ray tracing instructions areexecuted by the ray tracing circuitry. For example, the instructions maybe dispatched and executed on the FUs described above (e.g., vector FU4910, ray tracing FU4912, etc) and/or the graphics cores 3130 or raytracing cores 3150.

When execution is complete for a ray tracing instruction, the resultsare stored at 5006 (e.g., stored back to the memory 3198) and at 5007the primary graphics thread is notified. At 5008, the ray tracingresults are processed within the context of the primary thread (e.g.,read from memory and integrated into graphics rendering results).

In embodiments, the term “engine” or “module” or “logic” may refer to,be part of, or include an application specific integrated circuit(ASIC), an electronic circuit, a processor (shared, dedicated, orgroup), and/or memory (shared, dedicated, or group) that execute one ormore software or firmware programs, a combinational logic circuit,and/or other suitable components that provide the describedfunctionality. In embodiments, an engine, module, or logic may beimplemented in firmware, hardware, software, or any combination offirmware, hardware, and software.

Apparatus and Method for Asynchronous Ray Tracing

Embodiments of the invention include a combination of fixed functionacceleration circuitry and general purpose processing circuitry toperform ray tracing. For example, certain operations related to raytraversal of a bounding volume hierarchy (BVH) and intersection testingmay be performed by the fixed function acceleration circuitry, while aplurality of execution circuits execute various forms of ray tracingshaders (e.g., any hit shaders, intersection shaders, miss shaders,etc). One embodiment includes dual high-bandwidth storage bankscomprising a plurality of entries for storing rays and correspondingdual stacks for storing BVH nodes. In this embodiment, the traversalcircuitry alternates between the dual ray banks and stacks to process aray on each clock cycle. In addition, one embodiment includes priorityselection circuitry/logic which distinguishes between internal nodes,non-internal nodes, and primitives and uses this information tointelligently prioritize processing of the BVH nodes and the primitivesbounded by the BVH nodes.

One particular embodiment reduces the high speed memory required fortraversal using a short stack to store a limited number of BVH nodesduring traversal operations. This embodiment includes stack managementcircuitry/logic to efficiently push and pop entries to and from theshort stack to ensure that the required BVH nodes are available. Inaddition, traversal operations are tracked by performing updates to atracking data structure. When the traversal circuitry/logic is paused,it can consult the tracking data structure to begin traversal operationsat the same location within the BVH where it left off. and the trackingdata maintained in a data structure tracking is performed so that thetraversal circuitry/logic can restart.

FIG. 51 illustrates one embodiment comprising shader execution circuitry4000 for executing shader program code and processing associated raytracing data 4902 (e.g., BVH node data and ray data), ray tracingacceleration circuitry 5110 for performing traversal and intersectionoperations, and a memory 3198 for storing program code and associateddata processed by the RT acceleration circuitry 5110 and shaderexecution circuitry 4000.

In one embodiment, the shader execution circuitry 4000 includes aplurality of cores/execution units 4001 which execute shader programcode to perform various forms of data-parallel operations. For example,in one embodiment, the cores/execution units 4001 can execute a singleinstruction across multiple lanes, where each instance of theinstruction operates on data stored in a different lane. In a SIMTimplementation, for example, each instance of the instruction isassociated with a different thread. During execution, an L1 cache storescertain ray tracing data for efficient access (e.g., recently orfrequently accessed data).

A set of primary rays may be dispatched to the scheduler 4007, whichschedules work to shaders executed by the cores/EUs 4001. The cores/EUs4001 may be ray tracing cores 3150, graphics cores 3130, CPU cores 3199or other types of circuitry capable of executing shader program code.One or more primary ray shaders 5101 process the primary rays and spawnadditional work to be performed by ray tracing acceleration circuitry5110 and/or the cores/EUs 4001 (e.g., to be executed by one or morechild shaders). New work spawned by the primary ray shader 5101 or othershaders executed by the cores/EUs 4001 may be distributed to sortingcircuitry 4008 which sorts the rays into groups or bins as describedherein (e.g., grouping rays with similar characteristics). The scheduler4007 then schedules the new work on the cores/EUs 4001.

Other shaders which may be executed include any hit shaders 4514 andclosest hit shaders 4507 which process hit results as described above(e.g., identifying any hit or the closest hit for a given ray,respectively). A miss shader 4506 processes ray misses (e.g., where aray does not intersect the node/primitive). As mentioned, the variousshaders can be referenced using a shader record which may include one ormore pointers, vendor-specific metadata, and global arguments. In oneembodiment, shader records are identified by shader record identifiers(SRI). In one embodiment, each executing instance of a shader isassociated with a call stack 5203 which stores arguments passed betweena parent shader and child shader. Call stacks 5121 may also storereferences to continuation functions that are executed when a callreturns.

Ray traversal circuitry 5102 traverses each ray through nodes of a BVH,working down the hierarchy of the BVH (e.g., through parent nodes, childnodes, and leaf nodes) to identify nodes/primitives traversed by theray. Ray-BVH intersection circuitry 5103 performs intersection testingof rays, determining hit points on primitives, and generates results inresponse to the hits. The traversal circuitry 5102 and intersectioncircuitry 5103 may retrieve work from the one or more call stacks 5121.Within the ray tracing acceleration circuitry 5110, call stacks 5121 andassociated ray tracing data 4902 may be stored within a local raytracing cache (RTC) 5107 or other local storage device for efficientaccess by the traversal circuitry 5102 and intersection circuitry 5103.One particular embodiment described below includes high-bandwidth raybanks (see, e.g., FIG. 52 ).

The ray tracing acceleration circuitry 5110 may be a variant of thevarious traversal/intersection circuits described herein includingray-BVH traversal/intersection circuit 4005, traversal circuit 4502 andintersection circuit 4503, and ray tracing cores 3150. The ray tracingacceleration circuitry 5110 may be used in place of the ray-BVHtraversal/intersection circuit 4005, traversal circuit 4502 andintersection circuit 4503, and ray tracing cores 3150 or any othercircuitry/logic for processing BVH stacks and/or performingtraversal/intersection. Therefore, the disclosure of any features incombination with the ray-BVH traversal/intersection circuit 4005,traversal circuit 4502 and intersection circuit 4503, and ray tracingcores 3150 described herein also discloses a corresponding combinationwith the ray tracing acceleration circuitry 5110, but is not limited tosuch.

Referring to FIG. 52 , one embodiment of the ray traversal circuitry5102 includes first and second ray storage banks, 5201 and 5202,respectively, where each bank comprises a plurality of entries forstoring a corresponding plurality of incoming rays 5206 loaded frommemory. Corresponding first and second stacks, 5203 and 5204,respectively, comprise selected BVH node data read from memory andstored locally for processing. As described herein, in one embodiment,the stacks 5203-5204 are “short” stacks comprising a limited number ofentries for storing BVH node data (e.g., six entries in one embodiment).While illustrated separately from the ray banks 5201-5202, the stacks5203-5204 may also be maintained within the corresponding ray banks5201-5202. Alternatively, the stacks 5203-5204 may be stored in aseparate local memory or cache.

One embodiment of the traversal processing circuitry 5210 alternatesbetween the two banks 5201-5202 and stacks 5203-5204 when selecting thenext ray and node to process (e.g., in a ping-pong manner). For example,the traversal processing circuitry 5210 may select a new ray/BVH nodefrom an alternate ray bank/stack on each clock cycle, thereby ensuringhighly efficient operation. It should be noted, however, this specificarrangement is not necessary for complying with the underlyingprinciples of the invention.

In one embodiment, a ray allocator 5205 balances the entry of incomingrays 5206 into the first and second memory banks 5201-5202,respectively, based on current relative values of a set of bankallocation counters 5220. In one embodiment, the bank allocationcounters 5220 maintain a count of the number of untraversed rays in eachof the first and second memory banks 5201-5202. For example, a firstbank allocation counter may be incremented when the ray allocator 5205adds a new ray to the first bank 5201 and decremented when a ray isprocessed from the first bank 5201. Similarly, the second bankallocation counter may be incremented when the ray allocator 5205 adds anew ray to the second bank 5201 and decremented when a ray is processedfrom the second bank 5201.

In one embodiment, the ray allocator 5205 allocates the current ray to abank associated with the smaller counter value. If the two counters areequal, the ray allocator 5205 may select either bank or may select adifferent bank from the one selected the last time the counters wereequal. In one embodiment, each ray is stored in one entry of one of thebanks 5201-5202 and each bank comprises 32 entries for storing up to 32rays. However, the underlying principles of the invention are notlimited to these details.

Apparatus and Method for Non-Local Means Filtering

“Non-local means” is an image processing technique for performingdenoising. Unlike “local” mean filters, which smooth an image bydetermining the mean value of a block of pixels surrounding a targetpixel, non-local means filters determine the mean value of all pixels inthe image, weighted by how similar these pixels are to the target pixel,resulting in improved post-filtering clarity and reduced loss of detailin the image compared with local mean algorithms.

In existing implementations, non-local means calculations are performedin software. As a result, these implementations are not suitable forreal-time rendering environments.

One embodiment of the invention uses the existing motion estimationhardware block in the media processor pipeline to perform non-localmeans filtering. Only a small amount of additional circuitry is requiredbecause the existing motion estimation circuitry is reused. Using thishardware, the embodiments of the invention can perform non-local meansfiltering far more efficiently when compared to current softwareimplementations.

In addition, because the motion estimation circuitry is inherentlyconfigured to evaluate motion across image frames, one embodiment of theinvention uses this feature to perform inter-frame non-local meansfiltering (rather than limiting operations to within the current frame).Thus, in one implementation, efficient and accurate temporal denoisingmay be performed using non-local means filtering within and/or acrossimage frames.

One embodiment of the invention includes a new interface comprisinghardware and/or software components (e.g., a programming interface) tomake the motion estimation block of the media processor accessible toother processor components and applications. By way of example and notlimitation, these may include depth estimation operations within a 3Dgraphics pipeline, deinterlacing of video images, and view interpolationfor virtual reality implementations.

Many of the processor architectures described herein include a mediaprocessor (sometimes referred to as a “media engine” or “mediapipeline”) with dedicated circuitry for encoding and decoding videocontent (e.g., such as H.264 video). These media processors includemedia pipeline 234 (FIG. 2B), media pipeline 316 (FIGS. 3A, 4 ), mediaengine 837 (FIG. 8 ), and media processing engine 3197 (FIG. 31 ). Eachsuch media processor includes circuitry to perform motion estimation forvideo processing. In one embodiment, instead of implementing amachine-learning approach to denoising, one embodiment of the inventionperforms non-local means filtering using the motion estimation hardwarewithin the media processor(s).

FIG. 53 illustrates one embodiment of the invention in which motionestimation circuitry 5320 of the media processing engine 3197 performsnon-local means filtering on rendered image frames 5330 to generatedenoised frames 5340. In particular, in one embodiment, the media API5302 (i.e., the set of commands/instructions capable of being executedby the media processing engine 3197) is supplemented with non-localmeans commands 5305 to implement the techniques described herein. Thenon-local means commands 5305 access various architectural features ofthe motion estimation circuitry 5320 including the additional poweroffered by the motion estimation circuitry 5320 to perform non-localmeans filtering across multiple image frames 5330, rather than merelywithin the current frame as in current implementations. For example, oneof the primary functions of the motion estimation circuitry 5320 whenperforming video coding is to identify the movement of pixel blocks(e.g., macroblocks, slices, etc) across successive frames. Thus, insteadof performing non-local means filtering for a target pixel bydetermining a mean of all pixels in a single image frame (weighted basedon similarity to the target pixel), the motion estimation circuitry 5320is used to determine a mean of all pixels spread across multiple imageframes.

The nonlocal means filtering may be performed at the level of pixels orpixel blocks (e.g., macroblocks). The pixel blocks may comprise anyarrangement of pixels supported by the motion estimation circuitry 5320including blocks with the same number of pixels in each dimension(16×16, 8×8, or 4×4) and blocks with a different number of pixels ineach dimension (e.g., 16×8, 16×4, 8×4, etc). Moreover, an image framemay be encoded with an asymmetric arrangement of pixel blocks such thatthe center region of the image frame is encoded at higher precision(e.g., using 4×4 pixel blocks) while the periphery of the image frame isencoded at lower precision (e.g., using 16×16 pixel blocks).

Regardless of the type of encoding used, in one embodiment, thenon-local means commands 5305 control the motion estimation circuitry5320 to distinguish the actual pixel data from “noise” in the imageframes 5330 to produce the denoised image frames 5340. In particular, inone implementation, using the motion estimation circuitry 5320,efficient and accurate temporal denoising is performed using non-localmeans filtering within and/or across image frames.

In operation, a command streamer 5310 streams image rendering commandsto the ray tracing circuitry 5110 (i.e., for traversal and intersection)and shader execution circuitry 4000 (i.e., for execution of shaders) togenerate the image frames 5330 using the various techniques describedherein. Subsequently, or concurrently, the command streamer 5310 streamsnon-local means commands 5305 to the media processing engine 3197 whichexecutes the commands with the motion estimation circuitry 5320 toevaluate successive image frames 5330 to perform denoising and generatedenoised image frames 5340.

One embodiment of the media API 5302 is equipped with additionalcommands to make the motion estimation block 5320 accessible to otherapplications. By way of example, and not limitation, these may includedepth estimation operations within a 3D graphics pipeline, deinterlacingof video images, and/or view interpolation (e.g., for virtual realityimplementations).

A method in accordance with one embodiment is illustrated in FIG. 54 .The method may be implemented within the context of the architecturesdescribed herein but is not limited to any specific processor or systemarchitecture.

At 5401 the command streamer streams commands to the ray tracingcircuitry and shader execution circuitry. At 5402, the ray tracingcircuitry and shader execution circuitry execute the commands to renderimage frames. At 5403, the command streamer streams non-local means(NLM) commands to the media processing circuitry and at 5404, thecommands are executed using motion estimation circuitry to performdenoising by evaluating inter-frame pixel blocks.

EXAMPLES

The following are example implementations of different embodiments ofthe invention.

Example 1. A processor comprising: ray tracing circuitry to execute afirst set of one or more commands to traverse rays through a boundingvolume hierarchy (BVH) to identify BVH nodes and/or primitivesintersected by the ray; shader execution circuitry to execute one ormore shaders responsive to a second set of one or more commands torender a sequence of image frames based on the BVH nodes and/orprimitives intersected by the ray; and a media processor comprisingmotion estimation circuitry to execute a third set of one or morecommands to perform non-local means filtering to remove noise from thesequence of image frames based on a mean pixel value collected acrossthe sequence of image frames.

Example 2. The processor of example 1 further comprising: a commandstreamer to stream the first set of commands to the ray tracingcircuitry, the second set of commands to the shader execution circuitry,and the third set of commands to the media processing block.

Example 3. The processor of example 2 wherein the third set of commandscomprise an extension to an application programming interface (API)associated with the media processing block.

Example 4. The processor of example 1 wherein the non-local meansfiltering of a target pixel comprises determining a mean valueassociated with all pixels across the sequence of images, weighted bysimilarity to the target pixel.

Example 5. The processor of example 1 wherein the shader executioncircuitry comprises a plurality of execution units (EUs) to execute aplurality of different shaders to render the sequence of image frames.

Example 6. The processor of example 1 wherein the mean pixel value isdetermined based on an evaluation of blocks of pixels spread across thesequence of image frames.

Example 7. The processor of example 6 wherein the blocks of pixelscomprise macroblocks.

Example 8. The processor of example 7 wherein a macroblock comprises oneor more of: a 16×16 pixel block, an 8×8 pixel block, a 4×4 pixel block,a 16×8 pixel block, an 8×4 pixel block, or a 16×4 pixel block.

Example 9. A method comprising: executing a first set of one or morecommands on ray tracing circuitry to traverse rays through a boundingvolume hierarchy (BVH) to identify BVH nodes and/or primitivesintersected by the ray; executing one or more shaders responsive to asecond set of one or more commands to render a sequence of image framesbased on the BVH nodes and/or primitives intersected by the ray; andexecuting a third set of one or more commands on a media processorcomprising motion estimation circuitry to perform non-local meansfiltering to remove noise from the sequence of image frames based on amean pixel value collected across the sequence of image frames.

Example 10. The method of example 9 further comprising: streaming thefirst set of commands to the ray tracing circuitry, the second set ofcommands to the shader execution circuitry, and the third set ofcommands to the media processing block.

Example 11. The method of example 10 wherein the third set of commandscomprise an extension to an application programming interface (API)associated with the media processing block.

Example 12. The method of example 9 wherein the non-local meansfiltering of a target pixel comprises determining a mean valueassociated with all pixels across the sequence of images, weighted bysimilarity to the target pixel.

Example 13. The method of example 9 wherein the one or more shaders areto be executed on a plurality of execution units (EUs) to render thesequence of image frames.

Example 14. The method of example 9 wherein the mean pixel value isdetermined based on an evaluation of blocks of pixels spread across thesequence of image frames.

Example 15. The method of example 14 wherein the blocks of pixelscomprise macroblocks.

Example 16. The method of example 15 wherein a macroblock comprises oneor more of: a 16×16 pixel block, an 8×8 pixel block, a 4×4 pixel block,a 16×8 pixel block, an 8×4 pixel block, or a 16×4 pixel block.

Example 17. A machine-readable medium having program code stored thereonwhich, when executed by a machine, causes the machine to perform theoperations of: executing a first set of one or more commands on raytracing circuitry to traverse rays through a bounding volume hierarchy(BVH) to identify BVH nodes and/or primitives intersected by the ray;executing one or more shaders responsive to a second set of one or morecommands to render a sequence of image frames based on the BVH nodesand/or primitives intersected by the ray; and executing a third set ofone or more commands on a media processor comprising motion estimationcircuitry to perform non-local means filtering to remove noise from thesequence of image frames based on a mean pixel value collected acrossthe sequence of image frames.

Example 18. The machine-readable medium of example 17 furthercomprising: streaming the first set of commands to the ray tracingcircuitry, the second set of commands to the shader execution circuitry,and the third set of commands to the media processing block.

Example 19. The machine-readable medium of example 18 wherein the thirdset of commands comprise an extension to an application programminginterface (API) associated with the media processing block.

Example 20. The machine-readable medium of example 17 wherein thenon-local means filtering of a target pixel comprises determining a meanvalue associated with all pixels across the sequence of images, weightedby similarity to the target pixel.

Example 21. The machine-readable medium of example 17 wherein the one ormore shaders are to be executed on a plurality of execution units (EUs)to render the sequence of image frames.

Example 22. The machine-readable medium of example 17 wherein the meanpixel value is determined based on an evaluation of blocks of pixelsspread across the sequence of image frames.

Example 23. The machine-readable medium of example 22 wherein the blocksof pixels comprise macroblocks.

Example 24. The machine-readable medium of example 23 wherein amacroblock comprises one or more of: a 16×16 pixel block, an 8×8 pixelblock, a 4×4 pixel block, a 16×8 pixel block, an 8×4 pixel block, or a16×4 pixel block.

Embodiments of the invention may include various steps, which have beendescribed above. The steps may be embodied in machine-executableinstructions which may be used to cause a general-purpose orspecial-purpose processor to perform the steps. Alternatively, thesesteps may be performed by specific hardware components that containhardwired logic for performing the steps, or by any combination ofprogrammed computer components and custom hardware components.

As described herein, instructions may refer to specific configurationsof hardware such as application specific integrated circuits (ASICs)configured to perform certain operations or having a predeterminedfunctionality or software instructions stored in memory embodied in anon-transitory computer readable medium. Thus, the techniques shown inthe figures can be implemented using code and data stored and executedon one or more electronic devices (e.g., an end station, a networkelement, etc.). Such electronic devices store and communicate(internally and/or with other electronic devices over a network) codeand data using computer machine-readable media, such as non-transitorycomputer machine-readable storage media (e.g., magnetic disks; opticaldisks; random access memory; read only memory; flash memory devices;phase-change memory) and transitory computer machine-readablecommunication media (e.g., electrical, optical, acoustical or other formof propagated signals—such as carrier waves, infrared signals, digitalsignals, etc.).

In addition, such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage device of a given electronic device typically storescode and/or data for execution on the set of one or more processors ofthat electronic device. Of course, one or more parts of an embodiment ofthe invention may be implemented using different combinations ofsoftware, firmware, and/or hardware. Throughout this detaileddescription, for the purposes of explanation, numerous specific detailswere set forth in order to provide a thorough understanding of thepresent invention. It will be apparent, however, to one skilled in theart that the invention may be practiced without some of these specificdetails. In certain instances, well known structures and functions werenot described in elaborate detail in order to avoid obscuring thesubject matter of the present invention. Accordingly, the scope andspirit of the invention should be judged in terms of the claims whichfollow.

What is claimed is:
 1. A processor comprising: ray tracing circuitry toexecute a first set of one or more commands to traverse rays through abounding volume hierarchy (BVH) to identify BVH nodes and/or primitivesintersected by the ray; shader execution circuitry to execute one ormore shaders responsive to a second set of one or more commands torender a sequence of image frames based on the BVH nodes and/orprimitives intersected by the ray; a media sampler to access a memoryand perform texture sampling operations on graphics data stored in thememory; and a media processor to encode and decode the sequence of imageframes, the media processor comprising motion estimation circuitry toexecute a third set of one or more commands to perform non-local meansfiltering to remove noise from the sequence of image frames based on amean pixel value collected across the sequence of image frames, whereinthe non-local means filtering of a target pixel in a current image framecomprises determining a mean value associated with all pixels across thesequence of image frames, including image frames before and after thecurrent image frame in the sequence, and pixels within each individualframe, the mean value weighted by similarity to the target pixel,wherein the mean value is determined based on an evaluation of blocks ofpixels across the sequence of image frames, each image frame encodedwith an asymmetric arrangement of the blocks of pixels such that acenter region of a given image frame is encoded at a higher precisionusing smaller pixel blocks than peripheral regions of the given imageframe which is encoded at a lower precision using larger pixel blocks.2. The processor of claim 1 wherein the third set of commands comprisean extension to an application programming interface (API) associatedwith the media processor.
 3. The processor of claim 1 wherein the shaderexecution circuitry comprises a plurality of execution units (EUs) toexecute a plurality of different shaders to render the sequence of imageframes.
 4. The processor of claim 1 wherein the blocks of pixelscomprise macroblocks.
 5. The processor of claim 4 wherein a macroblockcomprises one or more of: a 16×16 pixel block, an 8×8 pixel block, a 4×4pixel block, a 16×8 pixel block, an 8×4 pixel block, or a 16×4 pixelblock.
 6. The processor of claim 1, further comprising a first interfaceto make the motion estimation circuitry of the media processoraccessible to other processor components and applications includingdepth estimation operations within a 3D graphics pipeline, deinterlacingof video images, and/or view interpolation for virtual realityimplementations.
 7. The processor of claim 1, further comprising acommand streamer to concurrently stream the first set of commands to theray tracing circuitry, the second set of commands to the shaderexecution circuitry, and the third set of commands to the motionestimation circuitry of the media processor.
 8. A method comprising:executing a first set of one or more commands on ray tracing circuitryto traverse rays through a bounding volume hierarchy (BVH) to identifyBVH nodes and/or primitives intersected by the ray; executing one ormore shaders responsive to a second set of one or more commands torender a sequence of image frames based on the BVH nodes and/orprimitives intersected by the ray; performing, by a media sampler,texture sampling operations on graphics data stored in a memory;performing, by a media processor, encoding and/or decoding operations onthe sequence of image frames; and executing a third set of one or morecommands on motion estimation circuitry of the media processor toperform non-local means filtering to remove noise from the sequence ofimage frames based on a mean pixel value collected across the sequenceof image frames, wherein the non-local means filtering of a target pixelin a current image frame comprises determining a mean value associatedwith all pixels across the sequence of image frames, including imageframes before and after the current image frame in the sequence, andpixels within each individual frame, the mean value weighted bysimilarity to the target pixel, wherein the mean value is determinedbased on an evaluation of blocks of pixels across the sequence of imageframes, each image frame encoded with an asymmetric arrangement of theblocks of pixels such that a center region of a given image frame isencoded at a higher precision using smaller pixel blocks than peripheralregions of the given image frame which is encoded at a lower precisionusing larger pixel blocks.
 9. The method of claim 8 wherein the thirdset of commands comprise an extension to an application programminginterface (API) associated with the media processor.
 10. The method ofclaim 8 wherein the one or more shaders are to be executed on aplurality of execution units (EUs) to render the sequence of imageframes.
 11. The method of claim 8 wherein the blocks of pixels comprisemacroblocks.
 12. The method of claim 11 wherein a macroblock comprisesone or more of: a 16×16 pixel block, an 8×8 pixel block, a 4×4 pixelblock, a 16×8 pixel block, an 8×4 pixel block, or a 16×4 pixel block.13. The method of claim 8, further comprising concurrently streaming,via a command streamer, the first set of commands to the ray tracingcircuitry, the second set of commands to the shaders, and the third setof commands to the motion estimation circuitry of the media processor.14. A non-transitory machine-readable medium having program code storedthereon which, when executed by a machine, causes the machine to performthe operations of: executing a first set of one or more commands on raytracing circuitry to traverse rays through a bounding volume hierarchy(BVH) to identify BVH nodes and/or primitives intersected by the ray;executing one or more shaders responsive to a second set of one or morecommands to render a sequence of image frames based on the BVH nodesand/or primitives intersected by the ray; performing, by a mediasampler, texture sampling operations on graphics data stored in amemory; performing, by a media processor, encoding and/or decodingoperations on the sequence of image frames; and executing a third set ofone or more commands on motion estimation circuitry of the mediaprocessor to perform non-local means filtering to remove noise from thesequence of image frames based on a mean pixel value collected acrossthe sequence of image frames, wherein the non-local means filtering of atarget pixel in a current image frame comprises determining a mean valueassociated with all pixels across the sequence of image frames,including image frames before and after the current image frame in thesequence, and pixels within each individual frame, the mean valueweighted by similarity to the target pixel, wherein the mean value isdetermined based on an evaluation of blocks of pixels across thesequence of image frames, each image frame encoded with an asymmetricarrangement of the blocks of pixels such that a center region of a givenimage frame is encoded at a higher precision using smaller pixel blocksthan peripheral regions of the given image frame which is encoded at alower precision using larger pixel blocks.
 15. The non-transitorymachine-readable medium of claim 14 wherein the third set of commandscomprise an extension to an application programming interface (API)associated with the media processor.
 16. The non-transitorymachine-readable medium of claim 14 wherein the one or more shaders areto be executed on a plurality of execution units (EUs) to render thesequence of image frames.
 17. The non-transitory machine-readable mediumof claim 14 wherein the blocks of pixels comprise macroblocks.
 18. Thenon-transitory machine-readable medium of claim 17 wherein a macroblockcomprises one or more of: a 16×16 pixel block, an 8×8 pixel block, a 4×4pixel block, a 16×8 pixel block, an 8×4 pixel block, or a 16×4 pixelblock.
 19. The non-transitory machine-readable medium of claim 14,wherein the operations further comprise streaming, via a commandstreamer, the first set of commands to the ray tracing circuitry, thesecond set of commands to the shaders, and the third set of commands tothe motion estimation circuitry of the media processor.